The Evolutionary Deep Learning Model for Electrical Load Forecasting

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

The forecasting of short term electricity load plays a vital role in power system. It is essential for the power system's reliable, secure, and cost-effective functioning. This paper contributes significantly for enhancing the accuracy of short term electricity load forecasting. It presents a hybrid forecasting model called Gated Recurrent Unit with Ensemble Empirical Mode Decomposition and Boruta feature selection (EBGRU). It is a hybrid model that addresses the non-stationary, non-linearity and noisy issues of the time series input by using Ensemble Empirical Mode Decomposition (EEMD). It also addresses overfitting and curse of dimensionality issues of load forecasting by identifying the pertinent features using Boruta wrapper feature selection. It effectively handles the uncertainty and temporal dependency characteristics of load and forecasts the future load using deep learning based Gated Recurrent Unit (GRU). The proposed EBGRU model is experimented by using European and Australian Electricity load datasets. The temperature has high correlation with load demand. In this study, both load and temperature features are considered for the accurate short term load forecasting. The experimental outcome demonstrates that the proposed EBGRU model outperforms other deep learning models such as RNN, LSTM, GRU, RNN with EEMD and Boruta (EBRNN) and LSTM with EEMD and Boruta (EBLSTM).

An accurate electrical Short-term Load Forecasting (STLF) is an eminent factor in the power generation, electrical load dispatching and energy planning for the power supply companies, specifically in developing countries. This paper proposes a novel temporal feature selection-based Long Short-term Memory (LSTM) model developed by the combination of standard Artificial Neural Network (ANN) layer and LSTM for electrical short term load forecasting. The LSTM model has excellent capability of predicting the stochastic nature of an hour ahead electrical loads. The standard ANN layer consisting 11 neurons is used as an input to LSTM cells. Such a combination of ANN layer with LSTM was never proposed before. The proposed model accommodates variations in weather as well as temporal inputs like humidity, holidays, and date-time features in the hourly load data of the power supply company situated in Johor, Malaysia. This paper gives the insights of hyper parameter tuning to capture the more generalized electrical load patterns in the dataset without compromising the time complexity of the proposed model. The proposed approach was compared with five existing approaches, namely: ANN, LSTM model 1, LSTM model 2, LSTM model 3 and Convolutional Neural Network-LSTM (CNN-LSTM) using hourly load dataset of Johor. The experimental results demonstrate that the proposed approach outperformed the existing approaches in terms of root mean square error, mean absolute percentage error and Diebold-Mariano statistical inference test within 95% confidence interval.

The accuracy of electric load forecasting is crucial when working on applications in power grid decision-making and operation. Due to the non-linear and stochastic behaviour of customers, the electric load profile is a complicated signal. In this paper, authors propose machine learning based automated system for electricity load forecasting, taking into consideration various variable factors that have an impact on the future electricity load demand. Three machine learning algorithms are used for evaluation of the proposed framework. The algorithms are evaluated on electricity load data collected from eastern region of Ontario, integrated with the weather and population data of the region. The Light GBM algorithm comparatively performs best with mean absolute error of 0.156. The developed system can be used for more accurate and efficient load forecasting applications.

With the introduction of the deep learning large model TabTransformer for electric load analysis and forecasting, this study aims to compare the performance of traditional machine learning, deep learning, and Transformer-based sequential prediction algorithms on heterogeneous tabular data. We propose an interpretable deep learning large model architecture, Electric Power Load TabTransformer (EPLTT), tailored specifically to electric load forecasting with mixed heterogeneous tabular data. EPLTT leverages a Transformer architecture designed explicitly for tabular data processing, akin to GPT-series deep neural networks, efficiently handling complex structured data and providing an effective alternative to conventional models. First, we discuss the strengths and weaknesses of various algorithms addressing tabular learning problems. Subsequently, we demonstrate the configuration and training of EPLTT on real-world forecasting datasets. Finally, we achieve interpretability by applying class-weight adjustments to compensate for dataset imbalance during training. Experimental results indicate EPLTT’s superior performance over traditional models such as LightGBM regarding accuracy, precision, and specificity. Additionally, EPLTT exhibits enhanced adaptability and robustness in dataset preparation and complexity management, fulfilling practical application requirements for speed, generalization, and interpretability. The model’s vertical-domain competitiveness is validated against real-world power load forecasting scenarios, addressing class imbalance, missing values, and categorical data.

Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads

Electricity load data is usually significantly trending, cyclical, and stochastic in nature; it is also influenced by external factors such as weather, holidays, and socioeconomic activities. In addition, electricity loads usually have long‐time dependencies, such as daily, weekly, and yearly periodicity. To address these challenges, we propose an electricity load forecasting method that combines PCA‐PSO‐Kmeans++ clustering with improved depthwise separable convolution. This article consists of two parts: data processing and electricity load forecasting. The data processing part of this includes seasonal decomposition of raw data, the Pearson correlation coefficient to select suitable exogenous variables, manual feature processing of raw electricity load data, and cluster analysis of feature‐processed data. The electricity load forecasting part of the model is trained using an improved depthwise separable convolution that incorporates an attention mechanism and residual connection. Electricity load forecasting on datasets from the US and Nordic region. Experimental results show that clustering combined with improved depthwise separable convolution is more accurate and reliable in electricity load forecasting. Based on experimental results, we quantify the performance gain contributed by clustering relative to the strongest non‐clustered baseline and demonstrate that clustering combined with the improved DSC further enhances accuracy.

Deep learning algorithms can be used to design systems that report data on patients and deliver warnings to medical applications or electronic health information if there are changes in the patient's health. These systems could be created using deep Learning. This helps verify that patients get the proper effective care at the proper time for each specific patient. A healthcare decision support system was presented using the Internet of Things and deep learning methods. In the proposed system, we examined the capability of integrating deep learning technology into automatic diagnosis and IoT capabilities for faster message exchange over the Internet. We have selected the suitable Neural Network structure (number of best-hidden layers and activation function classes) to construct the e-health system. In addition, the e-health system relied on data from doctors to understand the Neural Network. In the validation method, the total evaluation of the proposed healthcare system for diagnostics provides dependability under various patient conditions. Based on evaluation and simulation findings, a dual hidden layer of feed-forward NN and its neurons store the tanh function more effectively than other NN. To overcome challenges, this study will integrate artificial intelligence with IoT. This study aims to determine the NN's optimal layer counts and activation function variations.

A novel neural network technique for electric load forecasting based on weather compensation is presented. The proposed method is a nonlinear generalisation of Box and Jenkins approach for nonstationary time-series prediction. A nonlinear autoregressive integrated (NARI) model is identified to be the most appropriate model to include the weather compensation in short-term electric load forecasting. A weather compensation neural network based on an NARI model is implemented for one-day ahead electric load forecasting. This weather compensation neural network can accurately predict the change of electric load consumption of the coming day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing more accurate load forecast with a 0.9% reduction in forecast error.

With the large-scale development of smart grids and the integration of renewable energy, the operational complexity and load volatility of power systems have increased significantly, placing higher demands on the accuracy and timeliness of electricity load forecasting. However, existing methods struggle to capture the nonlinear and volatile characteristics of load sequences, often exhibiting insufficient fitting and poor generalization in peak and abrupt change scenarios. To address these challenges, this paper proposes a deep learning model named CGA-LoadNet, which integrates a one-dimensional convolutional neural network (1D-CNN), gated recurrent units (GRUs), and a self-attention mechanism. The model is capable of simultaneously extracting local temporal features and long-term dependencies. To validate its effectiveness, we conducted experiments on a publicly available electricity load dataset. The experimental results demonstrate that CGA-LoadNet significantly outperforms baseline models, achieving the best performance on key metrics with an R2 of 0.993, RMSE of 18.44, MAE of 13.94, and MAPE of 1.72, thereby confirming the effectiveness and practical potential of its architectural design. Overall, CGA-LoadNet more accurately fits actual load curves, particularly in complex regions, such as load peaks and abrupt changes, providing an efficient and robust solution for short-term load forecasting in smart grid scenarios.

Electricity load forecasting is of high importance for electricity management. Modern power systems are complex and diverse, resulting in increased randomness and nonlinear factors of electricity load data, which greatly increases the difficulty of forecasting. This paper proposes a hybrid-deep-learning-based load forecasting method, named DCFformer (DFT-CNN-FEDformer), for short-term load forecasting (STLF) tasks. The method first employs the discrete Fourier transform (DFT) to denoise time-sequence data on electricity load, so that fluctuations caused by incidents can be reduced. Secondly, it utilizes a convolutional neural network (CNN) that produces sequences of local features extracted from the denoised time sequences. Thirdly, a FEDformer network is applied to perform load forecasting by using extracted feature sequences. In the experiments, we utilize datasets from three regional power systems or apparatuses to compare the proposed DCFformer with other approaches, and the results show that, under the same conditions, DCFformer outperforms the competitors in forecasting precision, which proves the significance of its performance and practicality.

Electricity load forecasting plays a critical role in ensuring the efficient allocation of resources, maintenance optimization, and uninterrupted power supply. The double exponential smoothing (DES) method is widely used in forecasting time series data due to its adaptability and robustness, particularly in handling linear trends without seasonal patterns. However, determining the optimal value of the alpha parameter in DES is crucial for accurate forecasting results. This study proposes the use of the Particle Swarm Optimization (PSO) algorithm to optimize the alpha parameter in DES for electricity load forecasting. PSO is a computational method that iteratively improves candidate solutions by moving particles in the search space based on simple mathematical formulas. By optimizing the alpha parameter using PSO, we aim to enhance the accuracy of short-term electricity load forecasts. Our results demonstrate that the PSO-optimized DES approach achieves a Mean Absolute Percentage Error (MAPE) of 2.89% and an accuracy of 97.11%, indicating significant improvements in forecasting performance. While the PSO algorithm provides promising results, future research may explore the application of other metaheuristic algorithms, such as the whale or orca algorithms, to further enhance the optimization of DES parameters for electricity load forecasting. This study contributes to the advancement of forecasting techniques in the power industry, facilitating more efficient power generation and distribution planning.

Electric power load forecasting is a crucial link in ensuring the safe, stable, and economical operation of power systems and achieving sustainable resource allocation. This paper reviews the research status and development trends of electric power load forecasting, classifying it into three categories based on time span: short-term, medium-term, and long-term, suitable for daily grid dispatch, monthly/quarterly operational planning, and long-term planning, respectively. Regarding influencing factors, external meteorological conditions and economic policies, as well as internal user behavior patterns and power system dynamic characteristics, all significantly impact load forecasting. Traditional statistical methods such as time series analysis, regression analysis, and exponential smoothing once dominated but are limited by model assumptions and their ability to handle nonlinear relationships. Machine learning methods like Support Vector Machines (SVM) and Random Forests (RF) overcome traditional limitations through feature interaction and data fusion capabilities. Deep learning methods such as Convolutional Neural Networks (CNN), Transformers, and Graph Neural Networks (GNN) further enable high-dimensional modeling of complex spatiotemporal patterns, driving prediction accuracy to new heights. Future research directions focus on cross-domain collaborative forecasting, breaking disciplinary barriers to build multimodal fusion networks, and responding to carbon neutrality goals by developing net load forecasting models adapted to new power system demands and prediction models coupled with carbon credit mechanisms. In summary, power load forecasting technology is shifting from traditional approaches towards multidisciplinary integrated innovation and will become the core engine for intelligent decision-making in new power systems, aiding the safe, economical, and low-carbon transformation of the grid.

Accurate electrical load forecasting is a necessary part of resource management for power generating companies. The better the hourly load forecast, the more closely the power generating assets of the company can be configured to minimize the cost. Automation of this process is a profitable goal and neural networks have shown promising results in achieving this goal. The most often used neural network to solve the electric load forecasting problem is the back-propagation neural network architecture. Although the performance of the back- propagation neural network architecture has been encouraging, it is worth noting that it suffers from the slow convergence problem and the difficulty of interpreting the answers that the architecture provides. A neural network architecture that does not suffer from the above mentioned drawbacks is the Fuzzy ARTMAP neural network, developed by Carpenter, Grossberg, and their colleagues at Boston University. In this work we applied the Fuzzy ARTMAP neural network to the electric load forecasting problem. We performed numerous experiments to forecast the electrical load. The experiments showed that the Fuzzy ARTMAP architecture yields as accurate electrical load forecasts as a back-propagation neural network with training time a small fraction of the training time required by the back-propagation neural network.

In this paper, an artificial neural network (ANN) based methodology is proposed to forecast electricity load and price. The performance of an ANN forecast model depends on appropriate input parameters. Parameter tuning of ANN is very important to increase the accuracy of electricity price and load prediction. This is done using mutual information and decision tree. After selecting best features for forecasting, these features are given to forecasting engine working on principles of recurrent neural network (RNN). For simulations, dataset is taken from national electricity market (NEM), Australia. Results show that the methodology has increased the accuracy of electricity load and price forecast. Whereas, the error rate of forecasting is lower than the other models for electricity load and price.