dataesr/scientific-paragraphs-categorization huggingface dataset:
Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely ''hourly load consumption of Malaysia '' as well as ''daily power electric consumption of Germany'', are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used.
In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83. 17% to 91. 18% for the German data and achieved 98. 23% accuracy in Malaysian data, which is an excellent result in load forecasting.
Different algorithms for load forecasting have been studied so far. The authors in  [6]  prepared an overview of different types of load forecasting methods. They focused on the difference between short-term, medium-term, and long-term load forecasting and the factors which affect them. The authors in  [14]  discussed a review of load forecasting with a focus on regression-based approaches. To forecast day-ahead hourly electricity load, they used two particular data sets from the University of New South Wales, one from the Kensington Campus and the Tyree Energy Technologies Building. The authors in  [7]  surveyed different deep learning models for short-term load forecasting. They evaluated seven deep learning models on three real-world data sets. They proved that there is no correlation between the complexity of models and golden results.
Even in some cases, simpler models can achieve better results in short-term load forecasting. The authors in  [15]  proposed a hybrid deep neural network consisting of a CNN module, an LSTM module, and a feature-fusion module and tested it on the 2-year electric load data sets from the Italy-North area. Their model was compared with some other machine learning models, including: Decision Tree, Random Forest, DeepEnergy, LSTM, and CNN coming with better results. However, even though they achieved good results from their proposed model, their used data set was not challenging. The authors did not challenge the model with a more complex data set or prediction in different time horizons. The author in  [16]  proposed a parallel CNN-RNN model to predict one day ahead of load consumption. Temperatures, holidays, hours of day, and days of the week were used as features for the historical load series and achieved better results than regression-based models, DNN and CNN-RNN.
1) THE EVALUATION OF ARIMA In ARIMA models, time-series data decompose into m series to eliminate hourly/daily seasonality within data. According to figures 10, 11 there is a daily seasonality in Malaysian data (every 24 hours), and weekly seasonality in German data (every 7 days). Therefore, instead of using simple ARIMA, seasonal ARIMA (SARIMA) is being used to carry out STLF. In order to find the parameters of SARIMA, Auto-arima function from pmdarima library in python was used and it tries to find the best number for parameters by carrying out a comparison among different parameters. For German data, ARIMA (5,1,0)(5,0,5,  [7] ) became the final models and ARIMA (1,0,1)(2,0,0,  [24] ) achieved best results for Malaysian data. Figures  18  and 19  illustrate predicted results for both data from ARIMA model.
6) THE EVALUATION OF LSTM In this article, the LSTM model studies the last 24 hours load consumption and predict the next hour consumption in Malaysian data. In contrast, in German data, in order to predict next-day load consumption, it studies the last 7 days and predicts next day data. It has one LSTM layer in terms of architecture, while one dense layer is used as output. This type of architecture calls Vanilla LSTM, a well-known network and widely used in different areas. Same with the fully connected network in the previous section, the used activation function is ReLU. The model is also trained by ADAM optimizer for Malaysian data in 200 epochs and RMSprop for German data in 150 epochs. This model proves that LSTMs are a powerful tool for STLF due to the accurate results that the LSTM model has achieved as well as their independence to auto-correlation of input data.
E. RESULTS The detailed experimental results are presented numerically in tables 2 and 3. As shown in these two tables, the PLCNet's MAPE and RMSE are the smallest, while the R 2 score is the highest. Regarding the largest error value, the MAPE and RMSE of ETS have the highest error value in both German and Malaysian data sets, where it has got 0. 36 and   Besides, it can be seen in the figures that the PLCNet has performed far better than other models, especially in the German data set. Since the Malaysian data is hourly data, many samples are available; thus, all the models can be trained well, while German data is a daily one, so all the models have been trained with fewer samples. It leads to letting PLCNet model shows its power more with an accuracy of 91. 18%, in the German case. After that, LSTM has performed well, and its accuracy is 83.
17%. Likewise, the accuracy of the PLCNet model for Malaysian data is the highest, too, 98. 23%. However, in this case, there is no remarkable difference between the most accurate one and the second one, where LSTM-CNN accuracy is 97. 49%. Regarding the run time in the tables, linear regression and SVR are the fastest in German and Malaysian cases. However, they are outperformed by deep learning models. Besides, ARIMA and ETS are two computational techniques that take significant time for training. Even though all deep learning models in the tables took much time to be trained compared to regression-based approaches, their acquired accuracy is acceptable. The difference between the training time in deep learning models depends on the number of epochs considered. Table  4  indicates the runtime per epoch of each deep learning model for both Malaysian and German data sets.
According to the tables 2 and 3, LSTM has the highest runtime, but the main reason is that this model needs more epochs to be trained and predict future load. It can be seen that in table 4 LSTM is faster than LSTM-CNN and DNN models in both data sets. However, the PLCNet results show that this model has the highest accuracy and lowest error amount. It is also the fastest model between deep learning models where the runtime per epoch in Malaysian data is 4. 5(s) and in German data is 0. 93(s). Therefore, it is proven that the novel hybrid STLF algorithm proposed in this article is practical and useful. Although LSTM has a good performance when dealing with timeseries, its accuracy in German data set, which does not have many samples, is not good enough. Therefore, the LSTM is not suitable for this kind of prediction.
F. STATISTICAL ANALYSIS A common approach to comparing the performance of the machine learning models is using statistical methods to select the best one. This section aims to compare the PLCNet, and LSTM results since both achieved acceptable results in both German and Malaysian cases. To provide statistical analysis of the PLCNet and LSTM results, these two models were run 10 times. In terms of visualization, figures 37 and 38 show the results histogram of the PLCNet and LSTM for Malaysian data set, respectively. In this section, the t-test is used to understand the achieved results are just some stochastic results or they are trustful to perform statistical analysis. This analysis works based on the null hypothesis. The null hypothesis is that two models (the PLCNet and LSTM) are similar to each other.
There is no difference between them, while the alternative hypothesis is that the two models perform differently. The considered significance level is 5%, so if the acquired P-value is less than   5%, the null hypothesis can be rejected. It can be concluded that the PLCNet performs better than the LSTM model. After carrying out some statistical analysis, the obtained P-value is 0. 0411 (or 4. 11%), less than 5%. Likewise, after applying the statistical analysis to the German data case, it can be concluded that the results from the PLCNet are not stochastic, where the obtained P-value is 0. 03019 (or 3. 019%). Figures  38  and 39  illustrate the histogram plot of the results.
G. DIFFERENT TIME HORIZONS Previous sections discussed some machine learning techniques to predict next time step load data consumption. In other words, at time t, they predicted the load data at time t + 1. Since the Malaysian data is hourly data and German data is a daily one, all the models predicted the next hour load of Malaysian data and the next-day German data load. This section aims to challenge the PLCNet in different horizons. In the Malaysian case, the PLCNet will predict the next 24 hours, next 48 hours, and next 10 days load data,  The Malaysian data is hourly data, so predicting one day ahead load data next 24 time steps should be predicted, leading to a subtle modification in the model's architecture. The last layer of the model, which is a dense layer, will have 24 neurons to provide the next 24 hours prediction.
2) RESULTS Tables  5  and 6  show the results of next days prediction. However, to have comprehensive knowledge in terms of the model's performance, the results of the next hour prediction are added to these tables again. In this table, A1, A2, A3 and A4 represent next hour, next day, next 2 days and next 10 days results. Even though forecasting future load data in longer time horizons is a challenging task, according to these tables, there is almost 4% difference between the accuracy of the next hour prediction and the next 10 days prediction which is an acceptable difference and the model has a good performance in Malaysian case. Likewise, in order to make sure the PLCNet has better performance rather than two other discussed deep learning   models including DeepEnergy  [20] , LSTM and CNN-LSTM, tables 7 and 8 are provided to compare the results of all these models in different time horizons in terms of RMSE and R 2 score.
3) GERMAN CASE Same as Malaysian data, the model is challenged through German data set but in different time horizons. It predicts the next 7 days, next 10 days, and next 30 days load data. The model looks back to 7 days ago to perform 7 days ahead prediction and to do this task it needs 7 neurons in its last layer. Figure  44  shows the result of using the PLCNet to forecast Germany's next 7 days load data. If the PLCNet can carry out an LTLF task, it can be introduced as a well-performance tool in load forecasting applications. To evaluate the LTLF task model's performance, this section aims to predict the next 30 days of German load data, and the results are shown in figure  46. Like previous sections, there are 30 neurons in the last layer of the model to predict future load data in terms of the model architecture.
4) RESULTS So far, the German data set's illustrative results have been shown, and in the following, the numerical results are available. Besides, the one day ahead prediction results are    available in 10 and 9 to demonstrate the comparison between different horizons for same data sets. The modeling's name B1, B2, B3, and B4 represent the modeling for 1, 7, 10, and 30 days ahead, respectively. These two tables indicate that there are not many differences between one day ahead prediction and 10 days ahead prediction. Still, it is a big challenge for the model to predict the next 30 days load series because the average accuracy for the next day prediction is 91. 31% while it is 82. 49% for the next 30 days prediction. The problem is that the German data is not a big data set, and it has only 2186 recorded samples, so it is difficult for the model to learn all the parameters well while looking back 30 previous steps and predicting the next 30 steps.
Fully connected networks also use the same approach as regression-based approaches. Because there is no constant threshold to find those lags that are suitable to be used as variables, this procedure (finding lags through AC plot) may lead to higher errors. ARIMA and ETS also are two well-known time-series analysis approaches. However, some parameters need to be tuned to work with these methods. This procedure needs numerous trials to find the best values for them. Furthermore, in timeseries methods, data must be analyzed to find out if they are stationary or not. In contrast, LSTM can achieve good results whether data is stationary or not. CNN-LSTM also is a hybrid model, which is used in various load forecasting studies. The PLCNet achieves the best results between all the discussed models where the accuracy increase from 83.
17% to 91. 18% for load data in a German case study. Likewise, for a Malaysian data set, the model's obtained accuracy is 98. 23% which is very high for time-series results and the RMSE is very low at 0. 031. In summary, the PLCNet improves the results remarkably for the German data set. Besides, while all the models have acceptable Malaysian data performance, the most accurate results come from the PLCNet. It is faster than other deep learning models to train both German and Malaysian data in terms of runtime. This improvement and highly accurate results, as well as a quick training process, prove that this novel hybrid model is a good choice for STLF tasks. The PLCNet model was also evaluated in different horizons, and it performed better than other deep learning models. The accuracy of the PLCNet in Malaysian experiments for different horizons is between 94.
Data In this study, we select 92 stations in the French Alps and Pyrenees based on a minimum availability of observations of 60 % (percentage of missing observations thus varies between 0 % and 40 %, with an average of 18 %). Forecasts and observations are available and reliable for these 92 stations presented in Fig. 1  for 22 winter seasons covering the period 1994-2016, where each winter season starts on 6 December and ends on 30 April of the following year (3218 d in total). The forecasts are obtained by a chain of ensemble numerical simulations. The 10-member reforecasts of the PEARP ensemble NWP  (Descamps et al. , 2015; Boisserie et al. , 2016)  are downscaled by the SAFRAN system  (Durand et al. , 1999)  to obtain a meteorological forcing adjusted in elevation. The Crocus multilayer snowpack model, part of the S2M modelling chain  (Vernay et al.
Implementation The QRFs are obtained using the function quantregForest of the package quantregForest in R (R Core Team, 2017). The splitting procedure described above can be constrained by different choices, e. g. a minimum number of observations in leaves. In this paper, we grow 1000 trees, which represent a sufficiently large number of trees to span a great variety of meteorological situations without demanding an unbearable computational effort. Different values for the parameters mtry, which specify the number of predictors randomly sampled as candidates at each split (usually small, i. e. less than 10) and nodesize which defines the minimum number of cases (days) in terminal nodes, have been tried, and the best performances being found for mtry = 2 and nodesize = 10 (see Sect. 6).
Predictive distribution For QRFs, the predictive distribution, given a new set of predictors x, is the conditional cumulative distribution function (CDF) introduced by  Meinshausen (2006)  as follows: F (y|x) = n i=1 w i (x)1({Y i ≤ y}), (1) where the weights w i (x) are deduced from the presence of Y i in a final leaf of each tree when one follows the path determined by x. In practise, the resulting forecast is a set of quantiles from F (y|x) obtained with the function predict. quantregForest from the R package quantregForest. Different quantiles are thus computed for synthetic graphical representations or for score calculations.
Acknowledgements. The authors would like to thank  Bruno Joly , who developed and ran the PEARP reforecast,  Matthieu Vernay , who developed and ran the SAFRAN downscaling of the PEARP reforecast and real-time forecasts, and  Michael Scheuerer , for providing the initial code of the EMOS-CSGD. CNRM/CEN and IN-RAE are part of  LabEX OSUG @2020 ( ANR10 LABX56 ). Financial support. This research has been supported by the  Horizon 2020 Framework Programme ,  H2020 European Institute of Innovation and Technology (PROSNOW ; grant no. 730203 ). Review statement. This paper was edited by  Olivier Talagrand  and reviewed by  Ken Mylne  and two anonymous referees.
We conclude this special issue on the Global Earthquake Forecasting System (GEFS) by briefly reviewing and analyzing the claims of non-seismic precursors made in the present volume, and by reflecting on the current limitations and future directions to take. We find that most studies presented in this special volume, taken individually, do not provide strong enough evidence of non-seismic precursors to large earthquakes. The majority of the presented results are hampered by the fact that the task at hand is susceptible to potential biases in data selection and possible overfitting. The most encouraging results are obtained for ground-based geoelectric signals, although the probability gain is likely small compared to an earthquake clustering baseline. The only systematic search on satellite data available so far, those of the DEMETER mission, did not find a robust precursory pattern.
The conclusion that we can draw is that the overall absence of convincing evidence is likely due to a deficit in systematically applying robust statistical methods and in integrating scientific knowledge of different fields. Most authors are specialists of their field while the study of earthquake precursors requires a system approach combined with the knowledge of many specific characteristics of seismicity. Relating non-seismic precursors to earthquakes remains a challenging multidisciplinary field of investigation. The plausibility of these precursors predicted by models of lithosphere-atmosphere-ionosphere coupling, together with the suggestive evidence collected here, call for further investigations. The primary goal of the GEFS is thus to build a global database of candidate signals, which could potentially improve earthquake predictability (if the weak signals observed are real and false positives sufficiently uncorrelated between different data sources).
Such a stacking of disparate and voluminous data will require big data storage and machine learning pipelines, which has become feasible only 474 The European Physical Journal Special Topics recently. This special issue compiled an eclectic list of non-seismic precursor candidates, which is in itself a valuable source of information for seismologists, geophysicists and other scientists who may not be familiar with such types of investigations. It also forms the foundation for a coherent, multi-disciplinary collaboration on earthquake prediction.
In cases where the null hypothesis is more complex, for instance with respect to the clustering of earthquakes, the horizontal axis stands for the prior probability of occurrence of the events in the chosen alarmed space-time domain. Using such a systematic approach, one can determine which sets of parameters allow for the best correlations between the reported anomalies and earthquakes. In case where the data come in sufficiently large volume, the best practice consists in training (i. e. optimizing) the model on some part of it, and then in selecting the best model. Thus, this model is applied to an independent validation dataset to check its score in the error matrix or the Molchan diagram. When one cannot define a proper validation dataset (because, for instance, it would feature less data), the best solution is to compare the earthquakes/anomalies correlations with results obtained using surrogate earthquake catalogs: the latter consist in various randomizations of the original dataset.
For instance, one may keep the location of all earthquakes the same, but randomly switch their occurrence times (and/or magnitudes if possible or useful). This process will destroy the joint space/time/magnitude earthquake structures, while keeping each of the marginal ones separately. Another possibility consists in generating purely random locations, occurrence times, and magnitudes (the latter still respecting the Gutenberg-Richter law). The geophysical signal remains untouched, so that the set of anomalies is still the same, but their correlation with surrogate seismicity should be removed. The model is then optimized on each random realization of the earthquake dataset, and the collection of best scores can be compared to the one obtained using the true catalog. Such tests have to be performed over a large number of different surrogate catalogs.
Due to the limited number of cases, 5, included in the study, the results might not be representative of the global data. For instance, the considered earthquakes are not uniformly distributed over the years (occurring between end of February and mid-September), which may lead to spurious correlations with the seasonal variations of the regional weather. Indeed, it is not sufficiently acknowledged (but is well-documented in Statistics) that spurious serial correlations often emerge from diverse neglected non-stationarities (e. g. the short review in Section 2 of  [88] ) Only a reproduction of the same study but on the full datasets (NASA portals for precipitation and the USGS archive for earthquakes, both global datasets) would allow one to validate or reject a claim of correlation between precipitation and earthquakes.
Optimal index and space-time interval of precursors and target earthquakes are tested using a binary classification scheme. An optimization based on geoelectric signal frequency band selection is also performed. The authors use the Molchan score as the metric to optimize (train) their model parameterization. They find a significant correlation between geoelectric and seismic signals on independent validation sets although no probability gain is estimated that would quantify if a precursory signal is strong or weak. They also define a new method to convert results obtained using a multi-station approach into a probabilistic map of earthquake occurrence. This new feature is unfortunately not tested on an independent dataset. Future investigations could aim at combining geoelectric data with other types of data such as seismicity, as also proposed by  [54, 56].
The platform is flexible enough to allow the users to define their own space, time and magnitude windows, as well as the number of predicted events. The platform allows one to compare on the same footing forecasts based on probabilistic approaches and predictions based on alarms. Together with the upstream building of a global database, this downstream feature defines the ultimate tool to rigorously monitor the set of precursors in order to distinguish those that are reliable and those that should be set aside. selection bias: despite the patterns being random, it is possible to find apparent anomalies prior to selected mainshocks (grey boxes). These events seem correlated compared to the other signals that occur much earlier, an example of finding a pattern where there is none; (ii) overfitting: fitting a flexible model (here a polynomial of degree six, in dark green) on some training set (signals on the left column) provides an accuracy of one, obtained by the definition of some scoring system and fixed threshold definition.