Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models. The current developments and modifications in electrical networks and infrastructures, including distributed energy resources (D.E.R.s), have supported the energy demand. These advancements have helped clean power production through renewables integration and have also increased electrical power systems’ complexity . It has Since E.L.F. is responsible for meeting the demand-supply gap, their accuracy is of utmost importance and paid immense attention . Many faIn E.L.F., short-term load forecasting (STLF) is a driving factor of day-to-day operations. It impacts operations, financial costs, and savings, which are considered essential. Several STLF models have been proposed using statistical, regression, and artificial intelligence (A.I.) ,5. BasedIn a Smart Grid (S.G.) environment, the electrical consumption patterns keep changing. These changes are observed due to different factors, but not limited to weather, processes, activities, and events. While these factors are not cyclic, the consumption pattern of electrical load differs from one hour to another throughout time. Since the data streams in the S.G. environment are continuous, pattern changes are observed on a large scale. Numerous E.L.F. models have been proposed, but when new and unknown parameters influence the electrical consumption pattern, the conventional machine learning (ML) models cannot observe the changes and stand obsolete to handle such issues. These models can be differentiated as below:Traditional/Shallow learning models: The shallow learning models (S.L.M.) can be described as the models designed for dedicated applications, industries, utilities, sectors, and regions. These models have a pre-defined set of features and functions that cannot update, change, or modify themselves over the requirement. Any change requirement needs to be set manually and offline for each change operation. Therefore, these models stand obsolete in continuous data streams in the S.G. environment. Thus, the deep learning (DL) models have replaced them. Still, it is challenging for researchers to discover new ways to improve precision and accuracy during the forecast and make models adaptive. Despite many DL methods proposed by different researchers, the accuracy and adaptability factors are still unsatisfactory.Back draw of Traditional Models: The recent machine learning models for electrical load forecasting comprise a set of trained features, and the same elements are kept constant while testing. Therefore, the models keep specific acceptable results until the input feature or application changes. However, when we observe a single or multiple changes in the input features in an online system, the model starts to deteriorate and produce poor forecasts. Such forecasts impact energy management and thus cause a loss in terms of millions of dollars in operations and maintenance. The change in feature could occur due to a change in database, application, sector, or modality since the features selected in recent ML models are not standardized for any particularity. Therefore, their importance could change with respect to time. Moreover, the electrical pattern observes continuous change depending on different factors. Therefore, considering those factors are of utmost importance, and their integration as essential features needs to be considered while designing the model.It is observed that different parameters have a different impact on load forecasting. This impact needs to be calculated before involving any parameter since it can either increase/decrease the accuracy or cause complexity in the model performance.Adaptable Machine Learning Model: Adaptability is a feature that transforms any machine learning model to adjust itself and perform self-regulations following the environment. This is a self-regulatory attribute responsible for dynamically adjusting the model concerning changes in data trends. These ML models can retain their precision and accuracy even after parameter, application, utility, region, and sector variations. The adaptability approach is categorized into the semi- and fully-adaptive approaches, with few dynamic changes by fundamental structure improvement and autonomous module incorporation for self-tuning as per required features.The existing ML models have the nature of modeling static data. Therefore, these models cannot capture the dynamic changes observed in input parameters for load forecasting in the S.G. environment and cannot update themselves automatically (neither the features nor the model). This directs them to generate poor forecasts, have model limitations, and have an inconsiderable error percentage in the results, causing a loss in terms of cost for operations and generations . The electrical consumption patterns in S.G. and D.G.M. are acyclic, for which the model needs to be updated automatically with new features and patterns. These changes in input parameters need to be automatically updated in the model because the parameter tuning could not be performed physically in a real-time environment. Therefore, along with the discussion of the deterioration of E.L.F. models, we have also proposed an adaptive model framework for our future works, which can update itself for variations in parameter, application, utility, region, and sector utilizing self-adaption strategies. The proposed framework can entertain the S.G. and D.G.M. environment changes, perform real-time correlation with different parameters, and utilize the input parameters that have a more influential impact on the load while discarding the irrelevant features. The difference between the previous and our proposed model lies in model adaptation, modality recognition, parameter defining concerning modality, and continuous up-gradation of the model with its relevant features in a real-time environment.In this paper, we have examined the performance deterioration of existing load forecasting models concerning S.G. and D.G.M. environments, considering simple parametric variations to support the theory and proof the same with the help of experimentation. The study confirmed the performance deterioration of forecasting models on three different datasets. The evaluation is based on forecasting results produced by the models conducted before and after the change in input parameters. Based on the experimental results and our observed limitations of existing load forecasting models. The study has proposed a novel adaptive framework to overcome the existing challenges experienced in S.G. and D.G.M. environments. To highlight the performance deterioration in E.L.F. models due to changes in parameters and modality.To highlight the importance of STLF in the S.G. and D.G.M. environment.To evaluate existing load forecasting models.To propose a novel adaptive framework to improve the model deterioration in E.L.F.To emphasize the importance of adaptive models in real-time E.L.F.The main contributions of this study can be summarized as follows:The study discusses the limitations that cause the model deterioration, and a novel adaptive framework is proposed to improve the model deterioration since conventional models are obsolete in such a dynamic environment. The proposed model is believed to contribute to the D.G.M. and S.G. environment, where operation and power management are susceptible tasks. The proposed framework is crucial to assist utilities, independent power producers (I.P.P.), industries, off-grid consumers, and Smart Grid management in forecasting energy demand. It is also believed to help manage it through efficient adaptability features to integrate D.G.M. and manage power under respective elements affecting the power generation. Thus, intelligent energy management will be enabled through the proposed novel framework to direct an intelligent implementation of D.E.R.s. It will help with electricity planning, generation, transmission, distribution, scheduling, operations, and efficient integration of multiple power sources for clean optimum power generation and consumption. The rest of the paper is distributed as Based on the intensive literature review, we conclude that the traditional methods deployed for load forecasting and associated factors such as generation, transmission, distribution, and operations and planning would require a more robust forecasting solution to be deployed for S.G. and D.G.M. In the era of continuous technological evolution, one hundred and ninety-three countries at the United Nations have agreed upon the agenda of the sustainable development goals (S.D.G.s). Out of these goals, the smart city is of crucial importance since it is estimated that today 54% of people live in cities worldwide, but by 2050 this proportion is estimated to reach 66%. The cities are already facing the issues such as power management, unsustainability, and some other fragile problems . In suchThe S.G. is comprised of D.G.M., which could include D.E.R.s. This further integration of different power resources supports system availability and increases robustness, but the system complexity also increases. Though the S.G. is an unprecedented opportunity to adapt IR 4.0 and update the energy industries into the era of reliability, availability, and efficiency that contributes to intelligent power systems , E.L.F. E.L.F. helps control and manage energy generation and reduces the behavior of energy fluctuation . Since lEfficient generation, transmission, and distribution of electricity,Power acquired from different generation modalities,Fast network restoration (in case of any disturbance),Operations and management cost reduction for utilities,Reliable integration of large-scale renewables.Though the transition period from conventional grids to Smart Grids is critical, the benefits are rewarding. Some of the benefits include: Since S.G. is dependent on accurate and efficient load forecasting, E.L.F. is treated as a critical operational task. Thus, it has been distributed into four categories concerning the forecasting time-domain long-term load forecasting (LTLF), medium-term load forecasting (MTLF), short-term load forecasting (STLF), and very short-term load forecasting (VSTLF) . SeveralIn S.G. and D.G.M. environments, the STLF  is a significant factor in day-to-day operations and planning of a power utility and critical component of an energy management system. The STLF approach is efficient in reducing financial costs and operational risks, and it impacts directly on savings. Therefore, it is given much prominence and treated as a critical problem in the competitive energy market ,12. FiguSeveral STLF techniques have been proposed, some of which have been classified as statistical methods, including linear regression, auto-regressive moving average (ARIMA), and exponential smoothing (E.S.) . Later iNumerous statistical, machine learning, and deep learning models have been proposed in the literature for different forecasting applications . Still, Some of the traditional load forecasting models include  where TiRai, S. and De, M.  presenteWu, T. and Wang, J.  discusseHafeez, G. et al.  proposedZheng J. et al.  proposedBouktif S. et al.  modeled Azeem A. et al.  presenteJahan, I. S. et al.  reviewedWhen the load forecasting environment kept improving, new D.E.R.s were introduced to the power generation system. It enhanced the generation capability but also increased the system complexity. Several adaptive models were proposed to accommodate those complexities, including , where DMohammed, N.A. and Al-Bazi A.  proposedWu, S. F., and Lee S. J.  presenteLaib O. et al.  presenteS-Medina, J. J. et al.  proposedGuo T. et al.  discusseParameter optimization and selection of suitable models are critical when dealing with E.L.F. Some other studies that proposed parameter optimization and dimension reduction to improve the implication of critical parameters were also presented ,37. StilIn the current challenging environment, the evolution of data occurs over time. The patterns and associated correlations evolve, and this phenomenon in ML is termed concept drift. A similar phenomenon occurs in dynamic electrical environments, which involves S.G. and D.G.M. Some other studies considering the concept drifts are:Jagait, R. K., et al.  presenteMadireddy S. et al.  proposedFekri, M.N. et al.  presenteNalacaci G. et al.  utilizedAmong the literature survey, Jameel S. et al. ,50,51 prFrom the literature, we can safely state that the load forecasting models face accuracy challenges due to parametric variations causing irregular consumption patterns, and non-or-partial adaptability in the models is a critical problem . Some reThe literature reveals many approaches used for load forecasting, whether as a single method, hybrid method, mixture of different techniques, or clustering and optimization methods for improved electrical load forecasting results. After an in-depth literature review, we found some most prominent load forecasting approaches used in literature either in singular form or with different techniques. It is also highlighted in tx, where “t” is the time constant. The number of hidden layers depends on the methodology.ANN has apprehended numerous applications due to their high skill in learning. These methods are popular for forecasting for their reliability and accuracy. An estimation of function could be performed using ANN. It consists of layers termed input, hidden, and output, shown in HN weighting a matrix of HW with a bias Hb vector, using activation For a single hidden layer consisting of Treating its input for the final output we receive:oT minimize the error criterion, the ANN throughout training changes biases and weights, which can be expressed as below function:tx as input at the current time, th−1 as output from prior LSTM, t−C1 as previous unit memory, th as current system output, tc as current unit memory, it as model input, ot as model output, and tf as forget gate.One of the deep recurrent neural network family members is LSTM, used in various models such as time series, recognition of speech, and language. LSTM models have outperformed other methods for learning from earlier stages which is vital for the future forecast. In contrast to a feed-forward neural network, LSTM consists of cycles of network activation, which feeds from the recent step as a network input, influencing current time predictions . Consequt−h1, only keeping the most relevant information, and the output gate takes a decision regarding out info from the current cell state. The equations controlling the cells of LSTM are as follows:The input gate decides to add info from the present input state of the cell. The forget gate’s decision includes removing the report from tC is the cell state, th is the hidden state, and σ is the sigmoid function. Thus, the system performs computation that decides the output and is dependent on input, past values, and past analyses, enabling the model to grasp different time-scaled information for current computation purposes.p and q values. A criterion, namely B.I.C., is used out of a set of models to select the optimal model used to reduce uncertainty and improve prediction accuracy. The lowest B.I.C. score is chosen as the best fit model [k’ represents the number of model parameters, ‘m’ sample size, and ‘L’maximizes the value of the function. Though the ARIMA model was modified with the help of B.I.C., it is not yet adequate to accommodate the electrical load without its combination with some other method that can change the absence of data compensation [When the data perceived in a process is not stationary, it must first be transformed into a static form. The variance in the ARMA and ARIMA model transforms time series into static by d-order difference before fitting the series (d represents order difference). ARIMA also has issues such as over or underfitting data due to it model .(10)BICensation .It is evident from the literature and our experimental results that most load forecasting models are static and cannot adapt to the changes. The changes could be for parameters, application, sector, region, modality, etc. Due to this deficiency, they do not improve their model accuracy and are dedicated to only designing static applications. Therefore, such models need manual modifications to update themselves whenever a new feature is introduced. Besides, every application has a dedicated model, which stands obsolete in a different environment. Moreover, the existing literature lacks in addressing a model which encompasses different generation modalities, fully adaptive forecasting, and fully adaptive real-time forecasting. A model that can accommodate the changes in input features, applications, modality, and regions can be sufficient for the S.G.M. and D.G.M. environment and possess the dynamic ability to enhance the model’s ability to adapt to multi-variable online and real-time environments. Therefore, such models are required can adapt themselves automatically without interruption and manual modifications. Considering such capabilities, we have proposed a novel framework to address the issues and challenges experienced by existing load forecasting models. The proposed approach is designed to obtain data from multiple sources. The data is then deployed to the classifier and outlier sub-module in its feature identification module to fulfill the initial D.G.M. requirements. Later, to record the old and new changes, a repository is formed which captures dynamic features and constantly compares itself with identification and classification to fulfill S.G. and D.G.M. requirements of continuous change interpretation.Furthermore, the online training module consistently receives the input from the output. The LSTM ensemble is designed in a multi-layer parallel working mode that evaluates the forecast and generates optimum results. Thus, all incoming data is assessed for new data, features, and other changes to be adapted if observed, making it a novel framework. The description of the proposed framework, its modules, and their working is detailed in the methodology section.The existing models cannot adapt to the dynamic changes observed in S.G. and D.G.M. environments. Such as input feature changes in a real-time deployed environment and observed differences following region, modality, sector, and application. Thus, these models stand obsolete in such vibrant scenarios. Therefore, due to its ensembled module capabilities and novelty of continuous adaptivity in features and observed changes in its environment, the proposed model stands unique in comparison to existing models. It is deduced that the traditional models stand obsolete, and the proposed adaptive models face various challenges to contend in such a dynamic environment. They pose challenges for accuracy and precision, effective energy management, planning and operations, and future load prediction. Therefore, it generated the need to investigate the recent models concerning the challenges encountered in the S.G. and D.G.M. environment, observe the model’s behavior, and produce forecasts with different variable shifts and parameter changes.To overcome the described adaptability challenges, this study is crucial to explore the E.L.F. models and observe their performance in a distinct environment where the data has multiple changes in terms of modality and parameter. Moreover, it discusses the existing challenges and the proposition of a new framework to overcome the current issues in E.L.F. models following S.G. and D.G.M.This section explores the existing load forecasting models regarding S.G. and D.G.M. and elaborates on the proposed tentative adaptive framework. The intuition behind this framework is to adapt the modality and variations in parameter and region features. It is evident from the literature that to achieve accuracy in E.L.F. models, the data under observation shall be free of noise, loss, and error to attain acceptable results. In this study, we have considered two open-source datasets provided with the name of American electric power (A.E.P.) ranging from 2004 to 2018 and New York City (N.Y.C.) ranging from 2012 to 2017. The third dataset belongs to the real environment of Universiti Teknologi PETRONAS (U.T.P.), ranging from Nov to Dec 2019The dataset comprises “hourly load data” in megawatts (M.W.s) but have different parameters.Due to the involvement of unidentical parameters, we have observed the change of performance in model accuracy with the evolution of parameters. Thus, the difference in parameters has caused the models’ performance deterioration. The study uses ARIMA, ANN, and LSTM models on each dataset. Therefore, the study is subdivided into three case studies for better understanding and description.The utilized database comprises three datasets; two of them are available online at Kaggle with descriptions provided below. Since the load data is time series, the analysis of such vital attributes is required. To observe those attributes, we have decomposed our datasets into ‘trend’ showing the stability and instability of data, ‘seasonality’ expressing the fluctuations occurring at a certain periodicity, and ‘residual/noise,’ which depicts the remains. Further details are discussed below:The dataset was obtained from the A.E.P., a power utility company covering 11 states in the United States and delivering energy to more than 5 million consumers. The collected data is in univariate time series and ranges from 31 December 2004 to 2 January 2018. The dataset was obtained from the N.Y.C., and it is a multivariate dataset that additional includes temperature and precipitation values and ranges from 2012 to 2017. This data set was obtained from the U.T.P. gas district cooling (GDC) department, which provides the rest departments’ electrical services. This is a real data set comprised of hourly load demand for November and December 2019. The dataset is multi-variable with load demand, temperature, humidity, and wind speed variables. The datasets were preprocessed to eliminate the existence of any loss, noise, or insufficient data. Such values were eliminated considering respective appropriate algorithms. Later, the datasets were decomposed to attain the daily, weekly, and monthly hidden features. The features were then compared according to their correlations and influence on the output. The most optimized features were obtained, and the rest were discarded. However, during the data preprocessing, it was also noticed that weekly and monthly features had a similar impact on all three datasets. Moreover, the influence of specific parameters in combination was more significant than as individuals.Framework: The developed framework was to analyze the behavior of the ARIMA model on different datasets to calculate the performance change along with respective factors. Moreover, to observe the model deterioration when applied to different datasets, sectors, and applications. The model comprised modules performing data processing, verifying the seasonality, and removing the seasonality. Later, the model estimates the model parameters, checks for any residuals, and performs testing of the model. Finally, to produce a forecast considering all three datasets with parameter variations over the period.Framework: The developed framework was based on the ANN architecture and is believed to provide the model performance and deterioration when input parameters vary with time. The process includes data preparation modular and forecasting modular. A brief pictorial is presented in t − 1 and t − 2 times the historical load. The scaled values are then processed for prediction.Data Preparation Modular: Databases provide the data, which are filtered for NaN and 0 values. Later the data is further preprocessed and scaled following the model requirements. Once the dataset is mounted, the input parameters are defined along with the data distribution with training, testing, and validation split with 70, 10, and 10, respectively. The input parameters are kept constant for initial simulations, and later the parameter changes are applied to observe the model behavior over time for the changed parameter. At this stage, the initial parameter change is limited to change in one parameter, including change in the Forecasting Modular: The forecasting modular comprises ANN architecture called neural networks and is inspired by the human brain. The neural networks are intelligent enough to learn from data to recognize the patterns, data classifications, and prediction of future values based on historical information. The architecture of ANN is discussed in detail in the literature review section.The ANN forecasting modular comprises two input, three hidden, and one output layer. The parameter optimization is performed along with minimization of the loss function to avoid overfitting. An overfit function fails to uncover the hidden features, due to which it performs poorly. Different combinations for layers were utilized for optimal performance, but better results were generated using 27, 18, and 18 combinations for hidden layers and a dropout of 0.2. The model ran with 25 epochs at a batch size of 100 with a linear activation function known as ‘Relu.’Framework: We developed the framework to examine the performance of existing E.L.F. models on different datasets, which explored the impact and factors affecting the accuracy of E.L.F. models and causing model deterioration. The process flow includes data preparation involving data cleaning, transformation, reduction, and feature extraction. Later it is introduced to the forecasting modular to produce the forecasting involving parametric tuning, error comparison, and forecast production, as depicted in In this case study, we considered the datasets mentioned above. We also observed the impact and performance of the forecasting model before and after parametric tuning. We noticed the improvements in the forecasting results using the long short-term memory (LSTM) model. The methodology flow is distributed into two modules, namely, data preparation and forecasting. Training Accuracy: We get the accuracy if we apply the model to training data.Training Loss: It indicates how well the model fits the training data.Validation Loss: It indicates how well the model fits new data.The model performance is observed on the below basis:Dataset Preparation Modular: The data are received from databases and forwarded for sorting to separate the null values from the dataset to improve its credibility. Later, the clean dataset is processed for data extraction of existing features and values such as load behavior daily, weekly, monthly, and yearly. Based on respective segments, the graphs are plotted to understand the load behavior concerning the different time ranges. For final processing, the received data is later considered for daily values, taken through average means. These values are scaled using Minmax Scaler. The absolute values are subsequently processed to forecast modular for further actions.Forecasting Modular: The forecasting modular comprises sub-modules, namely, data split, model development, parametric tuning, error measurement and comparison, and production of load forecast.Data Split: The dataset is split into training and testing, considering 70 and 30 percent ratios.Model Construction: The construction of the LSTM model is comprised of 4 layers with unit values of 64, 32, and 16 with 25 epochs, the batch value of 32, Adams as an optimizer, and Relu as the current activation function. The drop out layer is set with a value of 0.3 and dense with a unit value of 1.Parametric Tuning: The tuning of parameters is performed by selecting different combinations of values from Error and Comparison: After every simulation is performed, the values of tuning parameters are changed, and each simulation has its product of error measurement, which is then stored to compare with other simulations to decide the optimal parameters for E.L.F.Forecast: Once the different combinations of unique optimal parameters are obtained after continuous and regressive simulations. The results are compared, and the most effective combination of parameters is selected for producing the forecast.Due to the rapid change in electrical dynamics and exponential growth in load demand, there is a need for continuous developments in machine learning and deep learning models. These models are capable of self-learning and adaptation to changes. Though the researchers are covering some dynamics in other fields for the transformation of models in classification applications ,50,51, sSince electrical data is acyclic and the demand continuously varies depending on different factors, including meteorology, region, power modality, events, etc. . TherefoThe intuition behind this framework is to adapt the modality and variations in parameter and region features. In the proposed framework, the projected ensemble mechanism’s diversity helps to handle the new features’ possible arrival, specifically modality, parameters, and meteorological features. More precisely, this ensemble proposes a novel approach that contributes diversity to a simple yet effective ensemble system. Considering the existing research gaps in the proposed framework, we have integrated two different modules to introduce adaptive load forecasting. These modules incorporate the requirements to enhance and develop a novel adaptive model to fulfill the demand of modern load forecasting comprised of multi-modality generation and Smart Grid. Modular-1 consists of a data stream pipeline composed of different modalities and features, including the changing scenarios. Modular-2 is termed an adaptive ensemble framework modular, which is further sub-sectioned into three sub-modules: feature change identification, online training, variable weightage, and forecasting module. The detail of Data Stream Pipeline: The data stream pipeline is responsible for providing the data from input sources  to the adaptive framework after observing the feature change. This module further consists of feature change scenarios which could be one or many concerning the case.t − 1 may experience a change at time t − 2 . Such changes could occur as a completely new feature or a transformation of existing into new segments. Moreover, these changes could be experienced as single or multiple value changes.Feature Change Scenario: The first modular, namely the feature change scenario, takes input from the different generation modalities and sorts the features accordingly. This modular might experience a change of input features at the different time stamps from any or each input database involved. Thus, the set of features introduced at time Consequently, they cause the model deterioration in terms of its performance. Thus, examining the features and relevant changes at the initial stage is of utmost importance for the better performance of the model. Therefore, we capture these changes at an earlier stage to counter the impact on the model and work towards the timely provision of an appropriate solution.Adaptive Framework: This modular comprises three further modules , which complete the requirement of an adaptive framework for a dynamic and challenging environment. The modules could be further described as below:Feature Change Identification: The features received from the “feature change scenario” modular are now processed in the feature change identification modular. The modular is responsible for identifying the features and classifying them according to their presence or absence from the standard feature repository. The features resulting in negative compared to the standard feature repository are considered feature changes or new features. The modular involves feature classification and new feature recognition based on classified and outlier approach, later compared to the standard feature repository. Each feature is then identified, analyzed, and compared with the common feature repository to decide whether the feature is new or old. The features with favorable comparison travel to the old library whereas the features with negation are identified as new features and stored in the new feature repository.This phenomenon of new feature identification repeats itself after the standard period, and these identified variables are then trained accordingly. The introduced features are updated in the library to feed the ensemble LSTM model for feature selection. These new features, unidentified in traditional load forecasting models, are responsible for the model’s performance deterioration. Therefore, we have integrated this modular to identify the feature change phenomenon in our framework. This will help the model analyze the input parameters with respect to changing modality and produce acceptable results compared to traditional models. Variable Weightage Module: Once the change in feature is identified and compared with the features library. The element is later weighted along with the existing and new library to compare the consideration weightage, and final variables are processed for dimensional reduction to filter only relevant and essential features to be considered for load forecasting. This function could be a single or a combination of multiple strategies to decide the variable weightage depending on the factors related to the application of forecasting.Ensemble LSTM Forecasting Module: The forecasting module is built on a parallel concept working with multiple layers. It comprises parallel working LSTM modules that work in parallel to perform better forecasting to attain the desired results. LSTMs are believed to work better on large datasets, and due to their excellent performance, they produce acceptable results. However, we plan to introduce the modified LSTM network that will further enhance the capabilities and fit the requirement of multi-modality generation and S.G.s.The existing models do not accept multiple input sources of generation or changes in input parameters at different time intervals  system using natural gas as a primary fuel source. The electricity generated at the U.T.P. comprises two units of gas turbines that support U.T.P. demand, internal usage, and chillers usage for the air-conditioning system. The data provided insights into peak hours ranging from 8:00 a.m. to 9:00 p.m., whereas off-peak hours are from 10:00 to 7:00 a.m. The change combination of parameters used before and after comprised date, time, historical load, temperature, wind speed, and humidity. Furthermore, the system under observation could experience constraints such as noise, insufficient data, false data, or data anomaly. The current framework consists of a module for dealing with noise, but more implications are required for further improvement. However, during the deployment of the framework, respective modules need to be actively deployed along with the framework.Thus, this framework provides better results in a dynamic environment than existing load forecasting methods that stand obsolete in such environments, employing continuous updates on features, rigorous training of variables, and an ensemble of multiple multi-layer LSTM regression modules. Our future work is implementing the proposed framework to enhance load forecasting practically and lay the forecasting foundation for multi-modality generations.We explored three different datasets with ARIMA and observed their behavior over time. We have presented the results of all these three different data sets before and after the variable change in After reviewing several proposed models for load forecasting utilizing ANN ,33,42,44Therefore, the results presented in In this article, we reviewed studies that proposed LSTM models for electrical load predictions ,30,31,32The dataset of A.E.P. and N.Y.C. were distributed over two years and are open-source datasets, whereas the U.T.P. dataset is a real environment dataset available for only two months. The before variables included DateTime and load, and the after variables included temperature along with earlier variables. The LSTM model was first run with different combinations of activation functions and optimizers and tested on distinct varieties described in the modular forecasting section to optimize and tune the parameters for optimum results.It is to be noted that when we introduced a new change in the dataset, it was unrecognizable in 2 cases. In contrast, the third case decreased its output accuracy after introducing a new parameter. Therefore, to measure the importance of this new variable, we introduced it to the model manually and measured the error percentage before and after the introduction of the new variable. The introduction of a new variable has improved the forecasting accuracy in all three cases. Therefore, it is deduced that the models cannot notice the presence and importance of any newly introduced variable until or unless it is present in the model or altered manually, which affects the model performance. 2 score; however, the highest score is considered better, which shows the data fit percentage.The experimental results are presented in When we compare the performance of different models over the A.E.P. dataset, it is evident from the results that the ANN model has outperformed ARIMA, and LSTM results were the nearest. A similar situation was encountered in the N.Y.C. and U.T.P. datasets as well. The performance of models can be graphically observed in However, it is evident from the presented results that the performance of E.L.F. models deteriorates over time with different changes and is also dependent on the methodology adopted at development time. Moreover, it is also evident from presented facts and literature that the S.G. and D.G.M. environments are dynamic, and such traditional models will stand obsolete. Furthermore, the literature’s proposed adaptive models have limitations discussed in Therefore, an adaptive model framework is proposed in this study as future work, which will adhere to the adaptability limitations of features, parameters, modality, region, industry, utility, power sector, D.E.R.s, I.P.P.s, and application environment. The presented framework is believed to be significantly crucial in S.G. and D.G.M. environments where the data is highly nonlinear with dynamic behavior and consists of multiple different generation resources having various factors which impact the prediction accuracy of E.L.F. model.STLF has been of utmost importance for implementing S.G.s and integrating D.G.M.s. Several factors challenge accurate load forecasting, including meteorological and production sources. Moreover, the models that can incorporate the characteristic change over time and are adaptable are significant. Since the traditional models are obsolete in dynamic and real-time environments. Several models have been discussed, but industry, residential, utility, S.G.s, D.G.M.s, and D.E.R.s are the environments where an efficient adaptive E.L.F. model is of paramount significance.This paper investigated the limitations of recent models. It proposed a framework by combining a classifier approach and outlier detection to improve the feature change and identification scenario significantly. An LSTM ensemble working with feedback and continuous feature update will adapt the model to the outer environment, parameter, modality, and scenario change. Minimal studies explore or employ the online E.L.F. learning techniques and require sufficient modifications and diversification for dynamic environments.Despite the described advantages of the presented framework, some limitations include defining significant differences in training and forecasting in real-time due to non-stationarity and continuous data input. Furthermore, some challenges that we would like to address in future works include analysis of injection of unlabeled and labeled data, defining boundaries for noise removal modular, different parametric tuning incorporation, and defining standards for inclusion or exclusion of parameters.Change in input variables deteriorates the performance of load forecasting models.Input variables impact the rate of error, which is inconsiderable for the S.G. and D.G.M. environment.Change in input variables impacts error percentage depending on the parameter introduced.Consideration of month and week as variables has no significant impact on considered datasets.Meteorological variables profoundly impact the E.L.F. model performance, and no standard is defined for such parameters since the parameter change concerning region, sector, modality, and application.Traditional models stand obsolete in S.G. and D.G.M. environments. Thus, real-time adaptive models are required.By analyzing the experiments performed above, the following conclusions can be drawn:


TVIR database contains numerous important functional genes, including 5215 auxin genes, 2437 anthocyanin genes, 15 002 flowering genes, 79 830 resistance genes, and 2639 glucosinolate genes of 59 vegetables. In addition, 2597 N6-methyladenosine (m6A) genes were identified, including 513 writers, 1058 erasers, and 1026 readers. A total of 2 101 501 specific clustered regularly interspaced short palindromic repeat (CRISPR) guide sequences and 17 377 miRNAs were detected and deposited in TVIR database. Information on gene synteny, duplication, and orthologs is also provided for 59 vegetable species. TVIR database contains 2 346 850 gene annotations by the Swiss-Prot, TrEMBL, Gene Ontology (GO), Pfam, and Non-redundant (Nr) databases. Synteny, Primer Design, Blast, and JBrowse tools are provided to facilitate users in conducting comparative genomic analyses. This is the first large-scale collection of vegetable genomic data and bioinformatic analysis. All genome and gene sequences, annotations, and bioinformatic results can be easily downloaded from TVIR. Furthermore, transcriptome data of 98 vegetables have been collected and collated, and can be searched by species, tissues, or different growth stages. TVIR is expected to become a key hub for vegetable research globally. The database will be updated with newly assembled vegetable genomes and comparative genomic studies in the future.Vegetables are an indispensable part of the daily diet of humans. Therefore, it is vital to systematically study the genomic data of vegetables and build a platform for data sharing and analysis. In this study, a comprehensive platform for vegetables with a user-friendly Web interface—The Vegetable Information Resource (TVIR,  Cucumis sativus (cucumber) in 2009, and since then the genomes of several vegetable species have been sequenced , edna and 2639 glucosinolate genes in the genomes of 59 vegetable species . All of One of the most important RNA modifications in plants is m6A methylation. The function and characterization of m6A are a major focus in current plant research. The genes involved in m6A modification are highly conserved across different plants . TherefoA. thaliana, while in the miRBase database 3913 miRNAs were identified from 15 vegetables and A. thaliana. In TVIR database the bar or line charts clearly display the number of miRNAs in each species, making it easier for users to perform comparisons among different vegetable species. Furthermore, the hairpin sequences, mature sequences, and target genes of miRNAs were predicted and can be downloaded from TVIR database. Moreover, the structure of each miRNA is illustrated and shown in TVIR. Publications related to each miRNA and links to the NCBI database are also provided. The multi-select dropdown menu allows users to select species to search miRNAs according to their needs. Users can also select all organisms from the dropdown menu, which will then show the search for a certain miRNA in all species.To facilitate the study of vegetable miRNAs, information on miRNAs was collected from the sRNAanno and miRBase databases . In the In addition to vegetable genome data, the transcriptome data of 98 vegetables were collected and collated. Users can easily and quickly select the corresponding transcriptome data according to the Latin name of the species, the plant tissue, or the settings of different growth and development periods. In the retrieved information table, users are provided with accession numbers and links to transcriptome data, sample information, sequencing library, and author information. Moreover, the informationtable can be downloaded, providing useful data resources for theretrieval, collection, and analysis of gene expression of vegetable species.Four popular tools—Synteny, Blast, Primer Design, and JBrowse—are included in TVIR database to help users conduct comparative and functional genomic analyses. Two syntenic tools, the Multiple Collinearity Scan toolkit (MCScanX) and Python MCscan, are provided and users can conduct syntenic analyses among species with these tools. Both Python MCscan and MCScanX have online and database modes in TVIR database.For Python MCscan, users can upload bed and coding sequence (CDS) files for syntenic analysis in the online mode. The configuration files ‘layout’ and ‘seqids’ can be created according to the manual of the Python MCscan program. Depending on the user’s needs, this tool can perform a collinear analysis of two to four species at a time. For database mode, users only need to select two to four species in TVIR database, then the syntenic diagram is rapidly illustrated.For MCScanX, users can upload Blast and general feature format (gff) files for syntenic analysis in the online mode. The database also provides a visualization using the results of syntenic analysis, including gff and collinear files. Four syntenic types, including dot plotter, circle plotter, bar plotter, and dual synteny plotter, can be obtained by selecting different configuration files. For the database mode, users only need to select two species among the 59 vegetable species, and the corresponding syntenic diagram is rapidly displayed according to the selected drawing type.The Blast tool was included in TVIR database to facilitate sequence alignment. A user-friendly interface was created and Blast databases were constructed using CDS and protein sequences of the 59 vegetable species. All users can easily perform sequence alignment by uploading a Fasta format file or directly copying sequences to the frame. A Primer Design tool was deposited in TVIR to help researchers design primers for genes in the 59 vegetable species by entering the related gene accession number. Moreover, users can also design primers for their gene sequences with Fasta format. In addition, a JBrowse tool was built to show the genomic sequences and features of vegetable genes. This tool allows users to check the detailed information for selected genes on the corresponding chromosome or scaffold sequences.B. rapa, C. sativus, L. albus, R. sativus, S. lycopersicum, S. melongena, and Solanum tuberosum, were also provided. Finally, the simple sequence repeat (SSR) markers were identified from all the coding genes of each species, and they can also be downloaded from TVIR database. All these genomic datasets and resources facilitate rapid and easy comparative genomic analyses of vegetable crops.The genome sequences, gff, CDS, and protein sequences of each vegetable species can be obtained from TVIR database. Moreover, the pan-genomes of seven species, including The Resources section of TVIR provides information on the database, publication, yield, and planting area of related vegetables. Through the Resources page, users can quickly understand the research value and status of major vegetables, and rapidly obtain relevant vegetable data resources. In the Help section, a detailed manual for each interface of the TVIR database is supplied. Furthermore, e-mail addresses and phone numbers are provided to enable users to easily contact us.Brassica , and erasers [Alkylation repair protein-B (AlkB)] . WritersThe mature, hairpin sequences, and gff files of miRNAs were downloaded from sRNAanno and miRBase (Release 22.1) , 68. TheThe CasFinder pipeline was used to design the Cas9 target sites for CRISPR . FirstlyBased on the Django framework, the TVIR database was built with MySQL database management according to our previous reports , 73 and This work was supported by the Natural Science Foundation for Distinguished Young Scholar of Hebei Province (C2022209010), the National Natural Science Foundation of China , the Natural Science Foundation of Hebei (C2021209005), the Educational and teaching reform research and practice project of North China University of Science and Technology (L21106), and the China Postdoctoral Science Foundation .X.S. conceived the project and was responsible for project initiation. X.S. and T.Y. supervised and managed the project and research. The data collection and bioinformatics analyses were led by X.S., T.Y., X.M., Z.L., Z.W., and X.F. The database construction was led by X.S., T.Y., Z.L., and F.N. The manuscript was organized, written, and revised by X.S., T.Y., X.F., X.M., R.C., Y.Z., and J.R. All authors read and approved the manuscript.All materials and related data in this study are provided in the TVIR database and supplementary files. Other datasets are available upon request to the corresponding author.The authors declare no competing interests.Web_Material_uhac213Click here for additional data file.


Helicobacter pylori-related gastric ulcer (H. pylori-related GU) is one of the most common digestive system diseases that have received widespread attention from researchers. The purpose of this article was to analyze the research status and hotspots of H. pylori-related GU and to predict its future research directions.H. pylori-related GU published from 2012 to 2022 were retrieved from the Web of Science Core Collection (WoSCC). The analysis of knowledge maps and bibliometrics was done with CiteSpace 6.1.R2 Basic and VOSviewer 1.6.18.The article and review papers associated with World Journal of Gastroenterology published the most articles on the H. pylori-related GU field, and GUT was the journal with the most cited articles. Yamaoka Y from Japan was the most productive author, and Graham DY from the USA was the most influential author. A keyword and reference analysis showed that the hot topics of research were the mechanism of H. pylori and the treatment of H. pylori-related GU. The keywords that emerged in the recent 5 years were oxidative stress, probiotics, competitive acid blocker, vonoprazan, gut microbiota, and neutrophil-activating protein.A total of 2,971 articles were included in the study. Between 2012 and 2022, the number of papers published showed an increasing trend. China was the most prolific country, and the United States was the most influential country. Baylor College of Medicine had the largest number of publications and citations among publishing agencies. H. pylori-related GU has generally shown an increasing trend. The treatment and pathogenesis of H. pylori-related GU remain a hot topic of research. The treatment of H. pylori by oxidative stress and competitive acid inhibitor mechanisms, the influence of gastrointestinal flora on H. pylori, probiotic adjuvant therapy of H. pylori-related GU, and the immunoprotective effect of neutrophil activator protein could be popular research directions and trends in the future.Over the recent 10 years, research on  Helicobacter pylori (H. pylori) is a Gram-negative bacterium that selectively colonizes the epithelium of the gastric mucosa  is one of the most common diseases affecting humans. An unbalance in defense factors and detrimental factors is the primary cause of GU, which is characterized by rhythmic epigastric pain after eating , 2. Helic mucosa . It is ac mucosa . Approxi. pylori . It is kc cancer , 7. An i. pylori –10. H. pifficult . NowadayH. pylori-related GU are still absent. In this study, we conducted bibliometric research on H. pylori-related GU papers that were published from 2012 to 2022 and analyzed the research status, hotspots, and trends in the field. This study will hopefully provide valuable information and aid in future research on GU-related H. pylori.In bibliometric analysis, the literature is identified, developmental trends are clarified both objectively and visibly, the current status is determined, and the future development of the field is predicted . BibliomH. pylori-related GU and to propose research directions for the future. By using bibliometric analysis software, such as VOSviewer and CiteSpace, these questions were addressed through the following goals: (a) the number and timing of publications; (b) journals, institutions, and countries of publications; (c) domains of study; (d) citation and co-citation of publications; and (e) keywords used in publications.A major objective of this article was to review the current research status and hotspots of H. pylori-related GU research, including clinical, therapeutic drugs, risk factors, pathogenesis, and prognosis; and articles published in English due to their higher recognition. If an article met one or more of the following criteria, it was excluded: non-English text, retracted publication, letter, meeting abstract, editorial material, and articles irrelevant to the H. pylori-related GU.The papers were screened according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . The stuHelicobacter pylori) or H. pylori] and (Language = English) and (Document type = Article or Review), Publication date from “2012-01-01” to “2022-07-17.”The data were retrieved from the Web of Science Core Collection (WoSCC). The following was the search strategy: [TS = (gastric ulcer) or GU] and [TS = HP or  was used to measure the output capacity of an author, institution, or country in a field. The number of citations (Ncs) and the global citations score (GCS) were used to determine the growth of scientific impact . The H-iThe bibliometric mapping and clustering analysis were conducted using VOSviewer, which builds and visualizes bibliometric networks. Nodes represent publications, and lines show the strength of the relationship. By assigning nodes to different clusters, the same color indicates the nodes belonging to the same cluster and have a high correlation between them . Co-authA bibliometric tool, CiteSpace, identifies trends and hotspots in the publications and enables users to explore areas of expertise and emerging research topics within knowledge areas . In this1) was used to generate pictures, and Excel was used for data statistics and tabulation.Bioinformatics . H. pylori-related GU remained at a high level and showed a fluctuating upward trend in H. pylori-related GU is a major concern for researchers.The global trends in publications on H. pylori-related GU, and the number of publications issued by countries/regions is shown in A total of 115 countries/regions around the world contributed to the research on H. pylori-related GU research.As shown in H. pylori-related GU publications are listed. World Journal of Gastroenterology was the most productive journal with 129 publications, which was followed by Helicobacter (113) and PLOS One (78). As shown in GUT (IF: 31.793) published only 13 articles, it had the highest Nc in the field of H. pylori-related GU. While Gastroenterology and the American Journal of Gastroenterology had only a few articles, their citations were very high. A great deal of impact was caused by H. pylori on the field of GU in these journals. Besides, the association between cited and co-cited journals was analyzed using a dual-map overlay , next was Graham DY from Baylor College of Medicine in the United States with 30 articles (H-index: 21), and Sugimoto M from Tokyo Medical University in Japan with 20 articles (H-index: 10). Graham DY had the highest number of citations , followed by Malfertheiner P from University Medical Center Hamburg-Eppendorf in Germany with 3,698 times and Yamaoka Y with 1,024 times. It indicated that the studies of Graham DY, Malfertheiner P, and Yamaoka Y have attracted more attention from researchers.GUT, followed by the article written by Hooi JKY published in 2017 in the Gastroenterology, and the article written by Malfertheiner P published in 2017 in the GUT. The network of articles with high citation frequencies is shown in Lancet (288 times), followed by the article written by Malfertheiner P published in 2012 in the GUT (277 times), and the article written by Kusters JG published in 2006 in the Clinical Microbiology Reviews (274 times). The co-citation of cited references is presented in H. pylori infection, prevalence, and management. Cluster 3 (blue) was primarily concerned with gene expression. Therapy and resistance were the main concerns of cluster 4 (yellow). Cluster 5 (purple) mainly focused on the disease related to H. pylori infection. The average year of these keywords is shown in H. pylori-related GU. Keywords reflect the core content of the paper. The network visualization of the keywords is shown in H. pylori-related GU were collected through the WoSCC database. The bibliometric software, such as VOSviewer and CiteSpace, were used to visually analyze the temporal and spatial distribution of literature, keywords, and the citations and co-citations of publications. Additionally, VOSviewer and CiteSpace were used to clarify the research status, hotspots, and trends of H. pylori-related GU. This article aimed to provide a reference for researchers in the field of gastrointestinal diseases.In this article, the publications related to H. pylori-related GU papers showed an increasing trend between 2012 and 2022, and researchers have been paying close attention to it. Among the 115 countries that have published a paper on H. pylori-related GU, China was certainly the most productive country, followed by the United States, and Japan. The largest number of publications in China indicated that H. pylori-related GU has a high incidence rate in China, which may be related to the eating habits of the Chinese. In terms of affiliations, Baylor College of Medicine had the highest number of publications, citations, and H-index. Three of the top five agencies were from the United States, indicating that the United States has a strong academic ability in the field.In total, 2,971 articles were included in our analysis. H. pylori-related GU, the United States has the highest citation and H-index, indicating it has the greatest influence. This apparent superiority might be attributed to the leading academics with advanced research facilities and technologies. Although China has the highest number of publications, the number of citations was close to Japan and Germany. This means that Germany and Japan had a higher quality of research on H. pylori-related GU, and Chinese researchers should improve the quality of their work.In the field of H. pylori-related GU, fewer articles were published in journals with high IF. The World Journal of Gastroenterology published the most in H. pylori-related GU studies, and it was also the second most cited journal. There is no doubt that this journal played a crucial role in studying H. pylori-related GU. Remarkably, the GUT ranked first in terms of citations with 13 publications, and the Gastroenterology ranked fifth with five publications. Researchers interested in this field should pay closer attention to these journals since the articles in these journals were of high quality.An article with a high IF is considered to be of higher quality when it appears in a journal. The journals were mainly in the gastroenterology and hepatology, microbiology, and pharmacology and pharmacy fields. In the field of H. pylori-related GU fields and received 1,024 citations. Yamaoka Y mainly studies the virulence factors of H. pylori and its mechanism of action (H. pylori in different regions, and the pathogenicity of H. pylori is correlated with virulence factors (H. pylori-associated ulcers, and in one of his highest-cited articles, he followed up patients with ulcers who participated in randomized controlled trials to cure ulcers for 2 years and found that triple therapy could reduce ulcer recurrence rates and that most peptic ulcers associated with H. pylori infection were curable (H. pylori was reduced and proposed treatment suggestions and follow-up clinical experimental research directions for this condition, which provided great help for the treatment and clinical research of H. pylori (The most productive author, Yamaoka Y, has published 47 papers on f action . Based o factors . In addi curable . In anot. pylori .GUT in 2012 and 2017 were ranked first and third in GCS. In those two Consensus Reports, the author and experts made recommendations for the prevention, diagnosis, and treatment of H. pylori, which provided support for clinicians in the treatment of H. pylori-related disease (Gastroenterology in 2017 ranked second. Through the systematic review and meta-analysis, the authors found that there were differences in infection rates in different countries/regions and that more than half of the world’s population was infected with H. pylori (H. pylori have always been a hot issue in this field.Articles with high citations can reflect the issues of concern in a research field. The papers written by Malfertheiner P published in the  disease , 23. A p. pylori . It suggH. pylori-related GU research. It can be seen from the co-cited references timeline view that eradication therapy and immunotherapy were the common concerns in H. pylori-related GU. Lim JH and his team found that the efficacy of levofloxacin-based 3-line eradication therapy for H. pylori-associated peptic ulcers was similar to that of first-/second-line eradication therapies (H. pylori-related diseases and discussed the potential and possibility of VacA as a targeted immunotherapy for the treatment of H. pylori (H. pylori infection in the Three Navajo Nation Chapter Communities and found that H. pylori infection in the region was associated with households and the use of uncontrolled water sources (H. pylori resistance rates were 80.6% for clarithromycin, 71.7% for amoxicillin, 49.4% for metronidazole, 45.1% for levofloxacin, and 11.4% for tetracycline, especially in children who had previously received H. pylori eradication treatment (The keywords and co-cited references timeline view can reflect hotspots in the field of herapies . Fahimi . pylori . In the  sources . Le LTT reatment .H. pylori-related GU disease research continues to progress, the research hotspot is changing as well. Co-cited references with the most bursts, keyword average year view, and emergence map show hot issues and directions in current research. The cited literature studies in the past 5 years have highlighted the main research directions for H. pylori-related GU and are listed as follows: the treatment of antibiotic-resistant H. pylori-related Gus (H. pylori (H. pylori therapy (H. pylori infections. Based on oxidative stress mechanisms, Silvan JM and his team explored a treatment method for H. pylori infection (H. pylori is related to the gastric microbiota, which shows a decrease in gastric microbial diversity and an increase in the relative abundance of bacteria (H. pylori infection and to protect against H. pylori-associated stomach disorders. Research by Zhao et al. (2O2-induced biofilm formation in bacteria, which makes them multidrug resistant. Based on these keywords, it can be seen that H. pylori-associated GU research is focused on pathogenesis and treatment. There is no doubt that future studies will continue to focus on H. pylori pathogenesis and the efficacy and safety of therapeutic drugs.As ated Gus , 24, the. pylori , and the therapy . Among t therapy . The stu therapy  and his nfection , 39. Stubacteria , 41. In bacteria , bifidobo et al.  found thH. pylori-related GU, and the results were objective and reliable. It provided an overview of the status, trends, and hotspots in H. pylori-related GU research. However, there are still some limitations to the research presented in this article. Only the WoSCC database was analyzed, and only English publications were included, which may lead to selection bias.In this article, CiteSpace and VOSviewer were used to conduct a comprehensive bibliometric analysis of H. pylori-related GU are on the rise, and the disease is receiving widespread attention. The findings of the study indicated that China published the most articles, while the United States was the most influential country. The pathogenesis and treatment of H. pylori-related GU have always been hot topics of research. Research on oxidative stress mechanisms, competitive acid blocker, and neutrophil-activating protein may be at the forefront of future research. Besides the traditional antibiotic-based triple and quadruple therapies, other treatments can also be studied, such as natural medicine preparations. Essentially, this article provides a scientific and quantitative analysis of H. pylori-related GU research from a global perspective and suggests future research directions.Through bibliometric research, the global publications of CY analyzed the data and drafted the manuscript. CO and MZ carried out a literature search. JQ and MX drew the figures. HS reviewed and revised the manuscript. All authors produced the results of this study and gave final approval to the version submitted.


One hundred and ninety-five staphylococci were recovered, and 94% of isolates were resistant to at least one antibiotic. Eighty-five staphylococci were mecA gene-positive, and 40% of them harbored SCCmec IV and V. The genotype of Staphylococcus aureus (S. aureus) was ST25, and the dominant genotype of methicillin-resistant Staphylococcus epidermidis (MRSE) was ST59. Three new sequence types were assigned as ST840, ST841, and ST842. One (2%) frequently touched surface was contaminated by S. aureus, which suggested that environmental contamination occurred in the hospital in China. Nineteen (39%) frequently touched surfaces were contaminated by methicillin-resistant coagulase-negative staphylococci (MRCoNS), and 46% of HP carried MRCoNS. Varied staphylococcal species and antimicrobial-resistance rates were observed between staphylococci that were recovered from hospital personnel and frequently touched surfaces. The transmission of MRSE and S. aureus between hospital personnel and frequently touched surfaces was detected. Hospital items and personnel may act as reservoirs of antimicrobial-resistant staphylococci, and cleaning strategies should be carried out to decrease the dissemination of antimicrobial-resistant staphylococci in hospitals in China.Staphylococci are major hospital-associated pathogens, and the dissemination of methicillin-resistant staphylococci in hospitals poses a great challenge for managing hospital-acquired infections. Little is known about the dissemination of staphylococci recovered from the hospital environment and personnel in China. In this study, antimicrobial susceptibility tests,  Staphylococcus aureus (S. aureus) and coagulase-negative staphylococci (CoNS) pose a health threat to humans worldwide. In China, staphylococci (13.4%) are on third place as a cause of clinical infection , whi, whimecAS. epidermidis ST59, ST200, and ST210 were recovered from samples that were collected from the hands of HP as well as FTS, which suggested the transmission between the hands of HP and FTS in the hospital. S. epidermidis ST200 was reported to be associated with healthy individuals only, and ST210 was the genotype that was previously recovered from the hands of HP [S. epidermidis ST59 was the community-associated pathogenic clone in Asia [S. epidermidis ST59 in this study indicates that the HP and FTS were contaminated by pathogenic clones. Hospital environment screening should be included in regulatory monitoring. in Asia , and theS. epidermidis, and ST59, ST200, and ST210 were found in S. epidermidis that were recovered from samples of HP and FTS. In contrast, 12 sequence types were exclusively identified in FTS, and 17 sequence types were only identified in HP. However, most STs belonged to CC2, which is the dominant clonal complex in HP in China [S. epidermidis ST210 was identified in samples that were collected from HP and FTS, respectively, and the samples were both gathered from the emergency room. Thus, the cross-transmission of S. epidermidis between HP and FTS was highly plausible. Microbiota of patients might affect the microbiota of FTS [S. epidermidis that were isolated from FTS, such as ST4, ST325, ST337, ST344, ST374, ST788, ST826, ST878, and ST916, were not reported before. Diverse STs were identified in S. epidermidis that were recovered from the hospital environment, and attention should be paid to hospital environment monitoring.In this study, 32 sequence types were determined in mecA-genes carriage rate, and the sequence types differed between staphylococci recovered from HP and FTS. S. epidermidis STs identified in HP samples were associated with both community and hospital clones, while STs of S. epidermidis that were isolated from FTS were partially associated with hospital clones. The sources of some S. epidermidis isolated from FTS remain unknown, and attention should be paid to tracing the sources of clones that were disseminated in the hospital environment in China. S. aureus ST25 was identified in samples of HP and FTS, which suggested the transmission of S. aureus between HP and FTS. Three new MLST types were identified in this study. The limitation of this study was that no patients were recruited in this study; whereas, the cross-transmission of S. aureus and S. epidermidis between HP and FTS in the hospital was observed, and cleaning strategies should be carried out to decrease the dissemination of antimicrobial-resistant staphylococci in hospitals in China.In conclusion, species of staphylococci that were isolated from FTS were much more diverse than that from HP. The antimicrobial-resistance rate,