Segmented Forecasting of Electric Load Under Pandemic Period Based on the ESD-ABiLSTMQR Method

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.

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

In this paper, Deep Convolutional Neural Network (DCNN) is proposed for short term electricity load and price forecasting. Extracting useful information from data and then using that information for prediction is a challenging task. This paper presents a model consisting of two stages; feature engineering and prediction. Feature engineering comprises of Feature Extraction (FE) and Feature Selection (FS). For FS, this paper proposes a technique that is combination of Random Forest (RF) and Recursive Feature Elimination (RFE). The proposed technique is used for feature redundancy removal and dimensionality reduction. After finding the useful features DCNN is used for electricity price and load forecasting. DCNN performance is compared with Convolutional Neural Network (CNN) and Support Vector Classifier (SVC) models. Using the forecasting models day-ahead and the week ahead forecasting is done for electricity price and load. To evaluate the CNN, SVC and DCNN models, real electricity market data is used. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the models. DCNN outperforms compared models by yielding lesser errors.

In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.

Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to enhance operational cost and efficient management. Load and price forecasting gives future trends. In literature many data-driven methods have been discussed for price and load forecasting. The objective of this paper is to focus on literature related to price and load forecasting in last four years. The author classifies each paper in terms of its problems and solutions. Additionally, the comparison of each proposed technique regarding performance are presented in this paper. Lastly, papers limitations and future challenges are discussed.

8 - Dynamic Electric Load Forecasting

Accurate load and price forecasting is one of the crucial stage in Smart Grid (SG). An efficient load and price forecasting is required to minimize the large difference among power generation and consumption. Accurate selection and extraction of meaningful features from data are challenging. In this paper, New York Independent System Operator (NYISO) six months' load and price data is used for forecasting. Decision Tree (DT) is used for feature selection and Recursive Feature Elimination (REF) technique is used for feature extraction. REF technique is used to remove redundancy from selected features. After feature extraction, two classifiers are used for forecasting. One classifier is Support Vector Machine (SVM) and other classifier is K-Nearest Neighbor (KNN). These classifiers have different parameters with some default values. Week ahead load and price forecasting is performed in this work. Accuracy of modified SVM is 89.5984% and modified KNN is 89.8605% is achieved for load forecasting. For price, accuracy of modified SVM is 88.2740% and modified KNN is 85.5999%.

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.

Nowadays electricity load forecasting is important to further minimize the cost of day-ahead energy market. Load forecasting can help utility operators for the efficient management of a demand response program. Forecasting of electricity load demand with higher accuracy and efficiency can help utility operators to design reasonable operational planning of generation units. But solving the problem of load forecasting is a challenging task since electricity load is affected by previous history load, several exogenous external factors (i.e., weather variables, social variables, working day or holiday), time of day, and season of the year. To solve the problem of short-term load forecasting (STLF) and further improve the forecasting accuracy, in this paper we have proposed a novel hybrid STLF model with a new signal decomposition and correlation analysis technique. To this end, load demand time series is decomposed into some regular low frequency components using improved empirical mode decomposition (IEMD). To compensate for the information loss during signal decomposition, we have incorporated the effect of exogenous variables by performing correlation analysis using T-Copula. From the T-Copula analysis, peak load indicative binary variable is derived from value at risk (VaR) to improve the load forecasting accuracy during peak time. The data obtained from IEMD and T-Copula is applied to deep belief network for predicting the future load demand of specific time. The proposed data driven method is validated on real time data from the Australia and the United States of America. The performance of proposed load forecasting model is evaluated in terms of mean absolute percentage error (MAPE) & root mean square error (RMSE). Simulation results verify that, the proposed model provides a significant decrease in MAPE and RMSE values compared to traditional empirical mode decomposition based electricity load forecasting.

Electricity load forecasting is an important part of power system dispatching. Accurately forecasting electricity load have great impact on a number of departments in power systems. Compared to electricity load simulation (white-box model), electricity load forecasting (black-box model) does not require expertise in building construction. The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation. Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics. Based on the real load dataset, this paper evaluates and compares the two mainstream short-term load forecasting techniques. Before the experiment, this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks (LSTMs) and Support Vector Machines (SVM) used in this paper. Secondly, based on the characteristics of the electricity load dataset, data pre-processing and feature selection takes place. This paper describes the results of a controlled experiment to study the importance of feature selection. The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting. The predictive accuracy of these models are calculated based on the error between the actual and predicted loads, and the runtime of the model is recorded. The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient. However, the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

Electric power system load forecasting plays an important role in the energy management system (EMS), which has great influence on the operation, controlling and planning of electric power system. A precise electric power system short term load forecasting will result in economic cost saving and improving security operation condition. With the development of deregulation in electric power system, the method of short term load forecasting with high accuracy is becoming more and more important. Due to the complicacy and uncertainty of load forecasting, electric power load is difficult to be forecasted precisely if no analysis model and numerical value algorithm model is applied. In order to improve the precision of electric power system short term load forecasting, a new load forecasting model is put foreword in this paper .This paper presents a short-term load forecasting method using pattern recognition which obtains input sets belong to multi-layered fed-forward neural network, and artificial neural network in which BP learning algorithm is used to train samples. Load forecasting has become one of the major areas of research in electrical engineering in recent years. The artificial neural network used in short-time load forecasting can grasp interior rule in factors and complete complex mathematic mapping. Therefore, it is world wide applied effectively for power system short-term load forecasting.

Rainer Gob, Kristina Lurz and Antonio Pievatolo (hereinafter GLP) address a very important issue in power systems management—load forecasting. Generally, load forecasting is concerned with the accurate prediction of the electric load (or demand) for specific geographical locations and over the different periods of the planning horizon. However, as in the discussed paper, the quantity of interest is usually the hourly total load, and the planning horizon is short-term—it ranges from a few minutes to a few weeks. Short-term load forecasting has become increasingly important since the rise of competitive energy markets. Electric utilities (called ‘energy vendors’ by GLP) are the most vulnerable as they typically cannot pass costs directly to the retail consumers. When electricity sectors were regulated, utility monopolies used short-term load forecasting to ensure the reliability of supply (prevent overloading, reduce occurrences of equipment failures, etc.). Nowadays, the costs of over-contracting/under-contracting and then selling/buying power in the balancing (or real-time) market are typically so high that they can lead to huge financial losses of the utility and bankruptcy in the extreme case. Load forecasting has become the central and integral process in the planning and operation not only of electric utilities (as GLP clearly point out) but also of energy suppliers, system operators and other market participants. A variety of methods and ideas have been tried for load forecasting, with varying degrees of success. Following Weron [1], GLP classify them into two broad categories: (i) statistical approaches, including similar-day (or naive), exponential smoothing, regression and time series methods; and (ii) artificial intelligence-based techniques. Among them, exponential smoothing stands out as a simple yet powerful approach, whereby the prediction is constructed from a weighted average of past observations with exponentially smaller weights being assigned to older observations. More complex variants—such as the Holt-Winters method—have been developed to model time series with seasonal and trend components [1, 2]. Application of exponential smoothing to hourly electricity load data requires further generalization to accommodate the prevailing seasonalities (daily, weekly and possibly annual) and weather-related exogenous variables (called ‘covariates’ by GLP). This discussion paper offers comments in three sections. The first section discusses the model and its position within the exponential smoothing literature on load forecasting. The second section comments on the empirical part of the paper. The final section offers recommendations for further research.

The COVID-19 situation has created an exceptional challenge in the power management system (PMS). This work mainly focuses on the load management through load forecasting. Power generation and distribution is the most important part of PMS. Accurate load forecasting can help to secure electricity scheduling, supply, and reduce the wastage of power. Right now, social distancing has created a great challenge to the administrators to run the power system efficiently and uninterruptedly with minimum involvement of human. In the sector of load management, it can be done through a proper and faster load forecasting approach. Electrical Load Forecasting through deep learning algorithm can perform an effective role in Power Management System (PMS). In this research real data is collected from West Zone Power Distribution Company Limited (WZPDCL) and meteorological data like temperature and humidity are collected from the website of Bangladesh Meteorological Department to train and forecast electrical load using MATLAB. Long-Short Term Memory (LSTM), Feed Forward Back Propagation (FFBP) and ELMAN Neural Network (NN) are used to forecast electrical load. As exogenous data, the load factor (L.F.), power factor (P.F.), current and temperature were used to train algorithms in forecasting the electrical load. A comparative analysis is shown to indicate which is the best suitable method for load forecasting of WZPDCL. Electrical load forecasting results are evaluated through Root Mean Square Error (RMSE). In this research for short-term electrical load forecasting, Feed Forward Back Propagation has shown a minimum RMSE value.

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.

With the evolution of the electricity market into a restructured smart version, load forecasting has emerged as an eminent research domain. Many forecasting models have been proposed by researchers for electricity price and load forecasting. This state of art introduces a load time series modeled with a hybrid technique culminating from the logical amalgamation of GARCH, a conventional hard computing method, Fuzzy ARTMAP, an artificial intelligence‐based soft computing technique, and wavelet transform, for treating the load time series. The study investigates into the ability of the proposed hybrid model in tackling the electricity load time series forecasting problems. The work under this study also includes comparisons drawn among models which use either one or two of the mentioned techniques and the model proposed. Results certify the efficacy and effectiveness of the model over others.