Optimizing electric load forecasting with support vector regression/LSTM optimized by flexible Gorilla troops algorithm and neural networks a case study

• Published in: Scientific reports Vol. 14; no. 1; pp. 22092 - 20
• Main Authors: Zhang, Zhirong, Zhang, Qiqi, Liang, Haitao, Gorbani, Bizhan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/166/987; Algorithms; Electric load forecasting; Energy demand; Flexible gorilla troops optimization algorithm; Forecasting; Forecasting techniques; Humanities and Social Sciences; Long short-term memory; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Support Vector Regression; Long short-term memory; Electric load forecasting; Flexible gorilla troops optimization algorithm; Support Vector Regression
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-73893-9

This research work focuses on addressing the challenges of electric load forecasting through the combination of Support Vector Regression and Long Short-Term Memory (SVR/LSTM) methodology. The model has been modified by a flexible version of the Gorilla Troops optimization algorithm. The objective of this study is to enhance the precision and effectiveness of load forecasting models by integrating the adaptive functionalities of the Gorilla Troops algorithm within the SVR/LSTM framework. To assess the efficacy of the proposed methodology, a comprehensive series of experiments and evaluations have been undertaken, utilizing authentic data obtained from 200 residential properties located in Texas, United States of America. The dataset comprises historical records of electricity consumption, meteorological data, and other pertinent variables that exert an impact on energy demand. The presence of this general dataset enhances the dependability and inclusiveness of the empirical findings. The proposed methodology was evaluated against various contemporary load forecasting techniques that are widely employed in the industry. The results of a comprehensive evaluation and performance analysis indicate that the modified SVR/LSTM model exhibits superior performance compared to the existing methods in terms of accuracy and robustness. The comparison results unequivocally demonstrate the superiority of the proposed method in accurately forecasting electric load demand.