STLF is essential to lower the cost of electricity transactions, encourage the reliable functioning of smart grids without interruption, and control loads as needed. STLF is also used to assess the risks of electricity shortages and reduce the appearance of loads to obtain a stable and reliable electrical network. This section will talk about the following subsections: short-term load forecasting for medium and large electrical networks, and short-term load forecasting for small electrical networks.
2.3.1. Short-Term Load Forecasting for Medium and Large Electrical Networks
The models proposed in [
29] is based on neural networks and particle swarm optimization (PSO) to evaluate the Iranian power system. The neural network-based solutions resulted in fewer prediction mistakes due to their capacity to adapt effectively the hidden properties of the consuming load. The accuracy of the proposed model was assessed based on the mean absolute percentage error (MAPE) which does not exceed 0.0338 and the mean absolute error (MAE) was found to be 0.02191.
The studies in [
30,
31] used empirical modal decomposition (EMD), where it makes the original electricity consumption data is first decomposed into several inherent mode functions (IMFS) with different frequencies and amplitudes. Researchers in [
30] suggested empirical mode decomposition gated recurrent units with feature selection for short-term load forecasting (EMD-GRU-FS). The Pearson correlation is used as the prediction model’s input feature to determine the correlation between the subseries and the original series. The experimental findings revealed that the suggested method’s average prediction accuracy on four data sets was 96.9%, 95.31%, 95.72%, and 97.17%, consecutively. Moreover, authors in [
31] enhanced a combination of integrated empirical modal decomposition (EMD) and long short-term memory network (LSTM) was presented for short-term load power consumption forecasting. The LSTM is used to extract features and make temporal predictions. Finally, on the end-user side, short-term electricity consumption prediction results were obtained by accumulating multiple target prediction results. The proposed EMD—LSTM method achieved MAPE of 2.6249% in the winter and 2.3047% in the summer.
Moreover, in China, a hybrid short-load forecasting system based on variation mode decomposition (VMD) and long short-term memory (LSTM) networks and optimized using the Bayesian optimization algorithm (BOA) has been developed [
32]. They compared the proposed methods with SVR, multi-layered perceptron regression, LR, RF, and EMD-LSTM, the result of the proposed method shows that MAPE is 0.4186% and R-squared is 0.9945. In [
33] a variational mode decomposition (VMD), temporal convolutional network (TCN), and error correction approach hybrid prediction model are suggested; where the train set is prediction error is used to adjust the model’s prediction accuracy. The hybrid model beats contrast models in prediction; the MAPE for 6, 12, and 24-step forecasting is 0.274%, 0.326%, and 0.405, respectively. The authors in [
34] employed the VMD-MFRFNN and DCT-MFRFNN algorithms to predict historical data, reducing volatility in the time series and simplifying its structure. They also compared them based on RMSE. The results indicated that the VMD-MFRFNN model was the best in predicting the historical data.
The researchers in [
35,
36,
37] used Artificial neural network (ANN) algorithms in building models for short-term electrical load forecasting since ANN algorithms deal with non-linear data. Ref. [
35] Proposed an ANN algorithm to make a robust computation with vast and dynamic data to cope with the difficulty of non-linearity of constructing historical load data for short-term load forecasting building energy consumption. The authors [
35] created and confirmed their results on a testbed home, which was supposed to be a real test facility. Their model was based on the Levenberg–Marquardt and newton algorithms and achieved a coefficient of determination within
is 0.91, which means the model is a perfect fitting with a rate of 90% of the variance in the power consumption variable predicted from the independent variable. Furthermore, researchers in [
36,
37] investigated the use of certain types of neural networks such as non-linear autoregressive exogenous (NARX) and convolutional neural networks (CNN) to improve the performance of standard ANN in handling time-series data. [
36] Suggested a novel version of CNN for the short-term load (one day ahead) forecasting employing using a two-dimensional input layer (consumptions from past states in one layer and meteorological and contextual inputs in the second layer). The model was used in an Algerian case study and the performance metrics indicated that MAPE and RMSE are 3.16% and 270.60 (MW) respectively. Ref. [
37] Proposed a model for load forecasting based on a non-linear autoregressive model with exogenous input (NARX) neural network and support vector regression (SVR) to forecast power consumption for the day ahead, a week ahead, and a month ahead at 15-min granularity, and they compared SVR and NARX neural network methods. Then, they evaluated the models with varied time horizons after training them with genuine data from three real commercial buildings. The SVR outperformed the NARX neural network model, according to their findings. For the day ahead, a week ahead, and a month ahead forecasting, the average predicting accuracy is approximately 93%, 88–90%, and 85–87%, respectively. In [
38] a novel multi-functional recurrent fuzzy neural network (MFRFNN) is proposed for developing chaotic time series forecasting approaches. They validated the efficacy of MFRFNN on real datasets to forecast wind speed prediction.
2.3.2. Short-Term Load Forecasting for Small Electrical Networks
STLF for small networks is becoming increasingly critical as the penetration of distributed and renewable energy grows. Having an accurate STLF for the small grid helps resource management of both renewable and conventional resources, as well as energy economics with electricity markets. As a result of the load time chain’s non-smooth and extremely unpredictable behavior in a small network, researchers in [
39,
40,
41] built models to predict short-term electrical loads for small networks based on real data obtained from smart meters in buildings. In [
39] an ensemble-based methodology for forecasting average construction consumption in France was proposed. The new framework basic learners are ensemble artificial neural networks (EANN), which are combined using multiple linear regression. Their findings revealed that stand-alone ANN performed better in terms of generalization ANN-based bagging artificial neural networks (BANN) with RMSE (WH) of 296.3437 and MAPE of 15.9396. A short-term electric load prediction model was built in London [
40], and online adaptive RNN technology was used, a load forecasting approach that can continually learn from fresh data and adapt to changing patterns. In this research [
40], the RNN is utilized to record time dependencies, and the online aspect is accomplished by changing the RNN weights based on fresh data. The result obtained indicated that the MAE is about 0.24 and 0.12 straight for 50 h ago and an hour ago respectively. Ref. [
41] Suggested an LSTM-based method in which they increased the prediction accuracy by tweaking the LSTM hyperparameters i.e., (learning rate, weight decay, momentum, and the number of hidden layers) using the Enhanced Sinusoidal Cosine Optimization Algorithm (ISCOA) and using data from India-Mumbai to forecasting long, medium, and short-term load. The obtained results [
41] for short-term forecasting give MAE = 0.0733, MAPE = 5.1882, MSE = 0.0115, RMSE = 0.1076.
Recurrent neural networks (RNNs), convolutional neural networks (CNNs), long short-term memory (LSTM), and deep belief networks have been used to enhance the precision of electricity load forecasts as reported in [
42,
43,
44]. In [
42] method for electrical load prediction based on historical data from China has been proposed. Whereas, the proposed model is a novel deep learning based on short-term forecasting (DLSF), and it was compared with the support vector machine (SVM) model. A deep CNN model was used to categorize the daily load curves. For STLF, an ANN with three hidden layers was utilized, taking into account different environmental elements such as temperature, humidity, wind speed, and so on. Simulation results indicated that the accuracy of DLSF is 90%, and 70% for the SVM. Ref. [
43] Proposed a model to know the uncertainty in the electrical load profiles, based on deep learning-machine learning algorithms to predict household loads in Ireland. A novel pooling-based deep recurrent neural network (PDRNN) has been used in which aggregates a set of customer load profiles into a set of inputs and is compared PDRNN with ARIMA, SVR, and RNN. The result showed that PDRNN method outperforms compared with ARIMA, SVR, and RNN as the RMSE (kWh) = 0.4505, 0.5593, 0.518, and 0.528 respectively. Ref. [
44] Proposed a model to predict short-term electrical loads at the residential level based on smart meter readings from the building. In this study [
45], the researchers suggested building a model to predict electrical loads at the level of small electrical networks The proposed model in the study [
45] is a CNN algorithm compared with SVM, ANN, and LSTM algorithms. Where the results showed the superiority of the proposed model CNN over SVM, ANN, and LSTM where RMSE = 0.677, 0.814, 0.691, 0.7 respectively.
A model in [
46] proposed to forecast short-term load forecasting for many families using Bayesian networks, the multivariate algorithm forecasts the next immediate household load value based on historical consumption, temperature, socioeconomic factors, and electricity usage. Its performance was compared to other forecasting algorithms using real data from the Irish intelligent meter project. The suggested technique delivers a consistent single forecast model for hundreds of families with varying consumption patterns, according to the results, MAE (kWh) is 1.0085, and the Mean Arctangent Absolute Percentage Error (MAAPE) is 0.5035. Recent experimental findings in [
47] suggest that the LSTM recurrent neural network yields lower prediction errors than statistics and other machine learning-based techniques. Ref. [
48] Built a model to forecast short-term load forecasting for individual residential households. Researchers in [
48] contrasted the LSTM model performance with the extreme learning machine (ELM), back-propagation neural network (BPNN), and k-nearest neighbor regression to show considerable prediction error reduction by employing the LSTM structure, and obtain Avg. MAPE aggregating forecasts of 8.18%, and an Avg. MAPE for individual forecasts of 44.39%.
Overall, predicting electrical loads in the short term helps in predicting loads for a few minutes, hours, a day, and sometimes a week, which helps in controlling the distribution of loads and evaluating the safety of the electrical network. However, based on the mentioned works, some researchers encountered difficulty in obtaining accurate data on the consumption behavior of consumers. In this section, we have reviewed some of the previous studies related to electric load forecasting. We started by reviewing some works related to the forecasting of short-term electrical loads, whether at the network level in regions as a whole or at the level of residential buildings, and then we discussed different algorithms used in forecastings such as LSTM, SVM, RF, CNN, ANN, and SVR.
To the best of the author’s knowledge, there is no forecast of electrical loads in the State of Palestine, and the prediction is different from country to another because the terrain and climatic conditions differ from one country to another as well as the population density and the power consumption. This research will focus on predicting short-term electrical loads based on the real dataset in Palestine. Using machine learning algorithms (LSTM, GRU, RNN) with the highest accuracy and least error rate will help to solve the problem of power outages in Palestine and save time and cost.