# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 2 Literature Review  
Several approaches for dealing with sparse and uneven time series data have been established throughout the years, allowing analysis to build predictive models. Nevertheless, some of the common statistical procedures for imputation, including mean, moving average, last observation, and simple regression might establish bias and loss of accuracy [45]. Time series problems have been solved using statistical models, such as the Bayesian network and Gaussian processes, as well as classic ML methods such as support vector regression and K-nearest neighbor (KNN) [46]. In time series modeling, a variety of contemporary ML models including unique applications of convolutional networks and transformers [47] have shown promising outcomes. The gated RNNs and their evolving architectures, on the other hand, remain favored methods for time series data. [PERSON] et al. [48] use a sequence autoencoder based on sequence-to-sequence models to produce latent representations of time series for future input to different classifier models, signifying the potential of pre-directed deep RNNs for time series categorization. In [38], a multi-layer perceptron model was utilized to anticipate wind power output 24 h ahead of time. In the meantime, researchers in [49] utilized the RNN to predict the PV power from a PV plant. In recent years, there has been a surge in interest in the usage of WSN in a variety of applications, which are designed and coded differently depending on the application. Before implementation, some aspects must be considered, such as the environment, the application's design objectives, cost, power consumption, hardware, and system constraints, to match the needs of the target applications [50,51]. Monitoring wind energy resources, which are the world's fastest expanding sources of power production today, is one of the most essential WSN applications, and there is a constant requirement to estimate the output power [52]. A study in [53] uses a neuro-fuzzy model-based fuzzy inference system to predict the generated power, and the prediction system uses the parameters measured by WSN. Authors in [54] used WSN to collect the meteorological data of wind power plants in real time. The improved fruit fly optimization algorithm and back propagation NN were used to improve wind power forecasting accuracy while retaining the stability and safety of the power system. The previous study in [55] utilized three unique techniques namely the ARIMA, radial basis function NN, and least squares support vector machine to forecast PV power output. Many ML techniques are employed in renewable energy forecasting, including linear regression, KNN regression, SVM regression, and decision-tree regression [56]. The most commonly employed ML algorithms were for solar and wind energy predictions. Statistical models offer more precise power forecasting predictions as real-time power generation data are taken into account and model parameters are constantly adjusted. The ARIMA offers a high degree of accuracy and a good ability to adapt to stationary and non-stationary time series. In [36], the ARIMA model was employed to predict the power output from the PV plant, considering the vulnerability to changing weather conditions. Despite the fact that the ARIMA model has evolved into the most general class of models for time series forecasting, it is unable to account for process behavior. The autoregressive moving average with the exogenous (ARMAX) model, which has been proven to be a useful tool in forecasting with time-series data, can be utilized to allow for exogenous inputs. One of the most popular solutions is to use renewable energy sources which have been proven to be sustainable, cost-effective, eco-friendly, abundant, non-polluting, and renewable [57]. The ML and artificial intelligence technologies have been applied in forecasting as computer processing speed becomes faster.