Short-term load forecasting utilizing a combination model: a brief review

• Main Authors: Ahmad, Faisul Arif, Liu, Junchen, Hashim, Fazirulhisyam, Samsudin, Khairulmizam
• Format: Article
• Published: Faculty of Engineering, Universitas Indonesia 2024
• Online Access: http://psasir.upm.edu.my/id/eprint/106244/; https://ijtech.eng.ui.ac.id/article/view/5543
• Institution: Universiti Putra Malaysia

To deliver electricity to customers safely and economically, power companies encounter numerous economic and technical challenges in their operations. Power flow analysis, planning, and control of power systems stand out among these issues. Over the last several years, one of the most developing study topics in this vital and demanding discipline has been electricity short-term load forecasting (STLF). Power system dispatching, emergency analysis, power flow analysis, planning, and maintenance all require it. This study emphasizes new research on long short-term memory (LSTM) algorithms related to particle swarm optimization (PSO) inside this area of short-term load forecasting. The paper presents an in-depth overview of hybrid networks that combine LSTM and PSO and have been effectively used for STLF. In the future, the integration of LSTM and PSO in the development of comprehensive prediction methods and techniques for multi-heterogeneous models is expected to offer significant opportunities. With an increased dataset, the utilization of advanced multi-models for comprehensive power load prediction is anticipated to achieve higher accuracy.