Short-term load forecasting utilizing a PSO and LSTM combination model:a brief review
In order to provide electricity to customers in a safe and economical manner, power companies face many 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 develo...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Engineering, Universitas Indonesia
2024
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/117767/ http://psasir.upm.edu.my/id/eprint/117767/1/117767.pdf |
| Summary: | In order to provide electricity to customers in a safe and economical manner, power companies face many 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 to STLF. In the future, in the work of combining LSTM and PSO, there will be a broad development space for comprehensive prediction methods and techniques of multi-heterogeneous models. On the basis of obtaining more data, the use of advanced multi-model for comprehensive prediction of power load will have higher accuracy. |
|---|