3 research outputs found
Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review
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