Short‐term load forecasting based on a generalized regression neural network optimized by an improved sparrow search algorithm using the empirical wavelet decomposition method

• Published in: Energy science & engineering 2023-07, Vol.11 (7), p.2444-2468
• Main Authors: Fan, Guo‐Feng, Li, Yun, Zhang, Xin‐Yan, Yeh, Yi‐Hsuan, Hong, Wei‐Chiang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Consumption; Decomposition; Economic development; Economic growth; Electric industries; Electrical loads; Electricity; Electricity consumption; Electricity distribution; electricity usage behavior; Empirical analysis; Fault tolerance; Forecasting; Forecasting techniques; generalized regression neural network (GRNN); Innovations; Mathematical models; Methods; Neural networks; Optimization; Search algorithms; short‐term load forecasting; Society; sparrow search algorithm (SSA); Supply & demand; Volatility; wavelet decomposition model (EWT); Wavelet transforms
• ISSN: 2050-0505; 2050-0505
• DOI: 10.1002/ese3.1465

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. Mode representation of power energy utilization processes and coupling integration effects of swarm intelligence (sparrow) methods.