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Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. [ABSTRACT FROM AUTHOR]

With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [ABSTRACT FROM AUTHOR]

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week's load, the previous day's load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R2) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R2 of 0.75 and MAPE of 5.70%. [ABSTRACT FROM AUTHOR]

Forecasting future electricity consumption is one of the critical processes for addressing energy management and supply–demand balance in modern electrical systems. In this paper, an adaptive hybrid ensemble, CMKP-EG-SVR, is presented to solve the short-term load forecasting problem. For this purpose, the merits of the median filtering are exploited at an initial phase of data preprocessing to reduce the high-frequency parts and to detect and correct outliers and missing data. Besides, Pearson's correlation, k-nearest neighbors algorithm and a particular calendar grouping based on day-type encoding are used as efficient tools for selecting relevant features and extracting similar patterns. To achieve diversification gains and improve the overall performance, several prediction engines based on support vector regression are proposed, in an adaptive weighting procedure, by our ensemble forecasting scheme. Finally, an error correction strategy based on a modified Gaussian function is also included for a further enhancement in prediction accuracy. The main idea behind this integration is to meet the multiple requirements and characteristics of accurate prediction using a unique and adaptive forecasting scheme. The electricity demand data from the Australian state of New South Wales are employed to describe an extensive evaluation under both normal and anomalous load conditions. The obtained results of the proposed CMKP-EG-SVR model demonstrate: (1) an average computation time of 4.47 s; (2) mean absolute percentage error values of the range of 1.03%–4.26%; (3) a superior performance over 24 state-of-the-art forecasting techniques. • A novel adaptive hybrid ensemble model is proposed for short-term load forecasting. • The model meets the multiple requirements of accurate forecasting. • A modified Gaussian function is offered to avoid error accumulation problem. • An extensive evaluation based on New South Wales load data is performed. • The model produced better results compared to 24 state-of-the-art existing methods. [ABSTRACT FROM AUTHOR]