Accurate Electric Load Forecasting Research Articles

Short-Term Electrical Load Forecasting Using an Enhanced Extreme Learning Machine Based on the Improved Dwarf Mongoose Optimization Algorithm

Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a “madness factor” is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm’s superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China’s 2016 “The Electrician Mathematical Contest in Modeling” demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy.

A Review of Deep Transfer Learning Strategy for Energy Forecasting

Over the past decades, energy forecasting has attracted many researchers. The electrification of the modern world influences the necessity of electricity load, wind energy, and solar energy forecasting in power sectors. Energy demand increases with the increase in population. The energy has inherent characteristics like volatility and uncertainty. So, the design of accurate energy forecasting is a critical task. The electricity load, wind, and solar energy are important for maintaining the energy supply-demand equilibrium non-conventionally. Energy demand can be handled effectively using accurate load, wind, and solar energy forecasting. It helps to maintain a sustainable environment by meeting the energy requirements accurately. The limitation in the availability of sufficient data becomes a hindrance to achieving accurate energy forecasting. The transfer learning strategy supports overcoming the hindrance by transferring the knowledge from the models of similar domains where sufficient data is available for training. The present study focuses on the importance of energy forecasting, discusses the basics of transfer learning, and describes the significance of transfer learning in load forecasting, wind energy forecasting, and solar energy forecasting. It also explores the reviews of work done by various researchers in electricity load forecasting, wind energy forecasting, and solar energy forecasting. It explores how the researchers utilized the transfer learning concepts and overcame the limitations of designing accurate electricity load, wind energy, and solar energy forecasting models.

Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network

Precise anticipation of electrical demand holds crucial importance for the optimal operation of power systems and the effective management of energy markets within the domain of energy planning. This study builds on previous research focused on the application of artificial neural networks to achieve accurate electrical load forecasting. In this paper, an improved methodology is introduced, centering around bidirectional Long Short-Term Memory (LSTM) neural networks (NN). The primary aim of the proposed bidirectional LSTM network is to enhance predictive performance by capturing intricate temporal patterns and interdependencies within time series data. While conventional feed-forward neural networks are suitable for standalone data points, energy consumption data are characterized by sequential dependencies, necessitating the incorporation of memory-based concepts. The bidirectional LSTM model is designed to furnish the prediction framework with the capacity to assimilate and leverage information from both preceding and forthcoming time steps. This augmentation significantly bolsters predictive capabilities by encapsulating the contextual understanding of the data. Extensive testing of the bidirectional LSTM network is performed using multiple datasets, and the results demonstrate significant improvements in accuracy and predictive capabilities compared to the previous simpleRNN-based framework. The bidirectional LSTM successfully captures underlying patterns and dependencies in electrical load data, achieving superior performance as gauged by metrics such as root mean square error (RMSE) and mean absolute error (MAE). The proposed framework outperforms previous models, achieving a remarkable RMSE, attesting to its remarkable capacity to forecast impending load with precision. This extended study contributes to the field of electrical load prediction by leveraging bidirectional LSTM neural networks to enhance forecasting accuracy. Specifically, the BiLSTM’s MAE of 0.122 demonstrates remarkable accuracy, outperforming the RNN (0.163), LSTM (0.228), and GRU (0.165) by approximately 25%, 46%, and 26%, in the best variation of all networks, at the 24-h time step, while the BiLSTM’s RMSE of 0.022 is notably lower than that of the RNN (0.033), LSTM (0.055), and GRU (0.033), respectively. The findings highlight the significance of incorporating bidirectional memory and advanced neural network architectures for precise energy consumption prediction. The proposed bidirectional LSTM framework has the potential to facilitate more efficient energy planning and market management, supporting decision-making processes in power systems.

Construction of a smart grid load forecasting platform based on clustering algorithm

Abstract With the rapid development of economic levels, the demand for electricity usage in various industrial sectors has continued to rise. In order to adequately meet the growth in demand for electricity usage at each user end within the power system, accurate forecasting of electricity load is required. This paper uses the K-Means algorithm as the basis, combined with the Canopy algorithm, whose simple and efficient advantages can be used as the initial clustering step for demanding clustering algorithms. The PCA algorithm is used to reduce the dimensionality of the residential multidimensional electricity consumption feature set, and after optimization, the feature vector weights are calculated to obtain the appropriate selection to determine the training and testing samples. The improved K-Means algorithm carries out five clustering tests, and its average accuracy is above 97%. The average relative error is then used as an indicator to compare the algorithm with the time series method, curve extrapolation method, and WKNR method for analysis. The average relative error of the smart grid load forecasting method proposed in this paper is 0.87%, while the average relative errors of the other three algorithms are 1.64%, 1.57%, and 0.93%, respectively. The distribution of relative errors falls less than 1% more than the other methods. It can be seen that the improved K-Means algorithm has higher prediction accuracy and also makes the smart grid load testing platform more practical due to its simple implementation principle.

Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks

The accurate short-term electric load forecasting (STLF) is critical for the safety and economical operation of modern electric power systems. Recently, the graph neural network (GNN) has been applied in STLF and achieved impressive success via utilizing spatial dependency between residential households to improve STLF. However, GNN based forecasting models require a large amount of training data to learn reliable forecasting models. For a newly built residential neighbourhood, the historical electric load data might be insufficient for the training of GNNs. Meanwhile, we can learn GNN based models on other areas, referred to as the source domains, with abundant data. In this paper, we propose to reuse the knowledge learned on the source domains to assist the model learning for an area that only a limited amount of data is available, referred to as the target domain. Specifically, we propose an attentive transfer framework to ensemble the GNN models trained from source domains and the GNN model trained on the target domain. The proposed framework can dynamically assign weights to different GNN based models based on the input data. Extensive experiments have been conducted on real-world datasets and shown the effectiveness of the proposed framework on different scenarios.

Research on Short-term Time Series Forecasting Method Based on Feature Fusion of Electric Power

In the power industry, time series forecasting has many application scenarios, such as accurate electricity consumption forecasting and load forecasting, which can provide a reference for power grid staff and reduce decision-making costs. To improve the accuracy of short-term load forecasting, this paper proposes a hybrid forecasting model based on an improved encoder-decoder architecture model and a Temporal Convolutional Network. Based on the gate recurrent unit, the model in this paper introduces the encoder-decoder architecture, adds the attention mechanism for improving the prediction accuracy, and performs feature fusion with the Temporal Convolutional Network, building a dual-feature fusion model finally. Experiments show that the method proposed in this paper reduces the evaluation metrics RMSE, MAPE and MAE by 7.7%, 15.2%, and 13.9% respectively compared to the Long Short-Term Memory Networks. Compared with the Temporal Convolutional Network, it is reduced by 2.6%, 4.8%, and 4%.

A Hybrid Residential Short-Term Load Forecasting Method Using Attention Mechanism and Deep Learning

Development in economics and social society has led to rapid growth in electricity demand. Accurate residential electricity load forecasting is helpful for the transformation of residential energy consumption structure and can also curb global climate warming. This paper proposes a hybrid residential short-term load forecasting framework (DCNN-LSTM-AE-AM) based on deep learning, which combines dilated convolutional neural network (DCNN), long short-term memory network (LSTM), autoencoder (AE), and attention mechanism (AM) to improve the prediction results. First, we design a T-nearest neighbors (TNN) algorithm to preprocess the original data. Further, a DCNN is introduced to extract the long-term feature. Secondly, we combine the LSTM with the AE (LSTM-AE) to learn the sequence features hidden in the extracted features and decode them into output features. Finally, the AM is further introduced to extract and fuse the high-level stage features to achieve the prediction results. Experiments on two real-world datasets show that the proposed method is good at capturing the oscillation characteristics of low-load data and outperforms other methods.

Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively.

Short-term electrical load forecasting through heuristic configuration of regularized deep neural network

An accurate electrical load forecasting is essential for optimal grid operation. The paper presents a methodology for the short-term commercial building electrical load forecasting through a regularized deep neural network: Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). Detailed heuristic analysis regarding relevant input feature selection, the volume of training data, hyperparameter tuning and regularizer selection of an optimal LSTM-RNN network configuration is presented. The regularized LSTM-RNN is used to forecast 30-min and 24-h ahead electrical loads of two commercial buildings in Virginia, USA. The forecast is performed for one week each over four different months in 2019: January, April, July and October to represent four different seasons in North America. The performance of electrical load forecasts has been compared against actual smart meter data from the electric utility of these buildings. For the case study presented, Mean Absolute Percentage Error (MAPE%) with the regularized LSTM-RNN is 4.9%, compared to 6.4%, 9.2% and 13.3% with Shallow-ANN (Artificial Neural Network), Support Vector Regression (SVR) and Linear Regression (LR) respectively for 30-min ahead electrical load forecast. For 24-h ahead electrical load forecast, MAPE (%) is 11.6%, compared to 12.7%, 13.4% and 14.3% with shallow-ANN, SVR and LR respectively. The methodology to configure a deep neural network (LSTM-RNN) for electrical load forecasting presented in this paper can be utilized for optimal forecasting performance.

Towards Electricity Price and Electric Load Forecasting Using Multi-task Deep Learning

The continuous development of the power Internet of Things (IOT) has enabled power market participants to obtain a large amount of data. Simultaneously, the power IOT has an increasing demand for power load and electricity price forecasting; Since the forecasting of electricity load and electricity price is a single task, and the model calculation accuracy is not high, this brings great challenges to the accurate forecasting of electricity load and electricity price. In this paper, two power load and electricity price forecasting models via multi-task deep learning are established perform high-precision joint forecasting of power load and electricity price Experimental results demonstrate that the prediction results of the proposed deep learning models are superior to the other compared approaches in terms of the main task and the auxiliary task, and show superior prediction performance, verifying the practicability and superiority of the power load and electricity price multi-task forecasting model.

A Novel Temporal Feature Selection Based LSTM Model for Electrical Short-Term Load Forecasting

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.

Short-term load forecasting based on LSTM networks considering attention mechanism

Reliable and accurate zonal electricity load forecasting is essential for power system operation and planning. Probabilistic load forecasts can present more comprehensive information for decision-making processes by quantifying the uncertainties of the electric load. A suitable feature selection is a critical step in forecasting, especially for data-driven methods. Weather conditions are another major factor related to electricity demand and play an important role in load forecasting. In this paper, we propose a dual-stage attention based long short-term memory (LSTM) network for short-term zonal load probabilistic forecasting. In the first stage, a feature attention based encoder is built to calculate the correlation of input features with electricity load at each time step. The most relevant input features can be adaptively selected. In the second stage, a temporal attention based decoder is developed to mine the time dependencies. Then, an LSTM model integrates these attention results and the probabilistic forecasts can be obtained using a pinball loss function. We also discuss how the proposed method can be utilized for feature and weather station selection. The effectiveness of the proposed method for both point and probabilistic forecasting is adequately verified on an open dataset of GEFCom2014, showing higher accuracy and generalization ability over other state-of-the-art forecasting models.

The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China.

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai'an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.

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

Accurate electric load forecasting is critical in guaranteeing the efficiency of the load dispatch and supply by a power system, which prevents the wasting of electricity and facilitates energy sustainability. Applications of hybrid intelligent computing methods and swarm-based algorithms with the support vector regression (SVR) model are very promising for solving the problem of premature convergence. This paper presents a novel SVR-based electric load forecasting model by hybridizing variational mode decomposition (VMD), the chaotic mapping mechanism, and the grey wolf optimizer (GWO) in the VMD-SVR-CGWO model to improve the solution searching experiences and to determine the appropriate combination of SVR’s parameters that improve forecasting accuracy. Numerical examples that involve two widely known electric load data sets reveal that the proposed VMD-SVR-CGWO model outperforms other models with respect to forecasting accuracy.

Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands.

An efficient management and better scheduling by the power companies are of great significance for accurate electrical load forecasting. There exists a high level of uncertainties in the load time series, which is challenging to make the accurate short-term load forecast (STLF), medium-term load forecast (MTLF), and long-term load forecast (LTLF). To extract the local trends and to capture the same patterns of short, and medium forecasting time series, we proposed long short-term memory (LSTM), Multilayer perceptron, and convolutional neural network (CNN) to learn the relationship in the time series. These models are proposed to improve the forecasting accuracy. The models were tested based on the real-world case by conducting detailed experiments to validate their stability and practicality. The performance was measured in terms of squared error, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). To predict the next 24 hours ahead load forecasting, the lowest prediction error was obtained using LSTM with R2 (0.5160), MLP with MAPE (4.97), MAE (104.33) and RMSE (133.92). To predict the next 72 hours ahead of load forecasting, the lowest prediction error was obtained using LSTM with R2 (0.7153), MPL with MAPE (7.04), MAE (125.92), RMSE (188.33). Likewise, to predict the next one week ahead load forecasting, the lowest error was obtained using CNN with R2 (0.7616), MLP with MAPE (6.162), MAE (103.156), RMSE (150.81). Moreover, to predict the next one-month load forecasting, the lowest prediction error was obtained using CNN with R2 (0.820), MLP with MAPE (5.18), LSTM with MAE (75.12) and RMSE (109.197). The results reveal that proposed methods achieved better and stable performance for predicting the short, and medium-term load forecasting. The findings of the STLF indicate that the proposed model can be better implemented for local system planning and dispatch, while it will be more efficient for MTLF in better scheduling and maintenance operations.

Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory

Electricity constitutes an indispensable source of secondary energy in modern society. Accurate and robust short-term electrical load forecasting is essential for more effective scheduling of load generation, minimizing the gap between generation and demand, and reducing electricity losses. This study proposes theory-guided deep-learning load forecasting (TgDLF), which is a gradient-free model that fully combines domain knowledge and machine learning algorithms. TgDLF predicts the future load through load ratio decomposition, in which dimensionless trends are obtained based on domain knowledge, and the local fluctuations are estimated via data-driven models. TgDLF simplifies the problem with the assistance of expertise, and utilizes the strong expressive power of neural networks to obtain accurate predictions. The historical load, weather forecast and calendar effect are considered in the model, and the model's robustness to inaccurate weather forecast data is improved by adding synthetic disturbance during the training process. Cross-validation experiments demonstrate that TgDLF is 23% more accurate than long short-term memory, and the TgDLF with enhanced robustness can effectively extract information from weather forecast data with up to 40% noise.

Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid

Accurate electric load forecasting is important due to its application in the decision making and operation of the power grid. However, the electric load profile is a complex signal due to the non-linear and stochastic behavior of consumers. Despite much research conducted in this area; still, accurate forecasting models are needed. In this article, a novel hybrid short-term electric load forecasting model is proposed. The proposed model is an integrated framework of data pre-processing and feature selection module, training and forecasting module, and an optimization module. The data pre-processing and feature selection module is based on modified mutual information (MMI) technique, which is an improved version of the mutual information technique, used to select abstractive features from historical data. The training and forecasting module is based on factored conditional restricted Boltzmann machine (FCRBM), which is a deep learning model, empowered via learning to forecast the future electric load. The optimization module is based on our proposed genetic wind-driven (GWDO) optimization algorithm, which is used to fine-tune the adjustable parameters of the model. The accuracy of the proposed framework is evaluated through historical hourly load data of three USA power grids, taken from publicly available PJM electricity market. The proposed model is validated by comparing it with four recent forecasting models like Bi-level, mutual information-based artificial neural network (MI-ANN), ANN-based accurate and fast converging (AFC-ANN), and long short-term memory (LSTM) in terms of accuracy and convergence rate.

Day-ahead load forecasting using improved grey Verhulst model

Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation.

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.

Forecasting Long-Term Electricity Demand Time Series Using Artificial Neural Networks

Accurate forecast of electricity load are increasingly important. We present a method to forecast long-term weather-dependent hourly electricity load using artificial neural networks. The fully connected dense artificial neural networks with 5 hidden layers and 1,024 hidden nodes per layer are trained using historic data from 2006 to 2015. Input parameters comprise calendrical information, annual peak loads and weather data. The results are benchmarked against the method to forecast electric loads used in the current mid-term adequacy forecasts published by the European Network of Transmission System Operators (entso-e). For validation year 2016, our approach shows a mean absolute percentage error of 2.8%, whereas the common approach as used by entso-e shows an average error of 4.8% using peak load scaling. Further, we conduct forecasts for Germany, Sweden, Spain, and France for scenario year 2025 and assess parameter variations. Our approach can serve to increase prediction accuracy of future electricity loads.