Optimization of Electricity Load Forecasting Model based on Multivariate Time Series Analysis

Abstract

Due to rising demand and expanding economies, forecasting electricity loads is vital for electrical system management. Precise forecasts assure both economic stability and effective utilization. The basis for generating schedules and managing energy is established by prediction, which is crucial for power stations and transmitting facilities. The purpose of this research is to develop an efficient load prediction approach. Hence, this study presents a novel fine-tuned backtracking search-driven log-sigmoid recurrent network (FBS-LRN) framework for improved thermal electricity load prediction. In the proposed framework, the FBS optimization strategy is introduced for recurrent network activated dynamically in long and short term memory (LSTM) with the log-sigmoid function. In the beginning, the FBS optimization approach is employed to improve the LSTM's characteristics to tackle the issue that the LSTM's performance will be impacted by the unpredictability of its internal properties. Next, using the Python platform, the electricity load projection framework, depending on the suggested FBS-LRN will be implemented into practice and examined using several criteria. The comprehensive research reveals that the suggested approach has superior prediction accuracy and efficacy compared to the current models. Planning for power production and use in the electrical system can be aided within thermal by higher-quality load forecasts.