RIME-LSTM Electricity Price Prediction Model Based on Variational Modal Decomposition

The objective of this study is to address the highly nonlinear, time-varying and non-stationary complex characteristics of electricity price. To this end, a RIME-LSTM electricity price prediction model based on variational modal decomposition (VMD) is proposed. The proposed methodology involves the decomposition of the electricity price sequence into multiple eigenmode components using VMD firstly. Each component is then trained and predicted by the RIME-LSTM network. Finally, the component prediction results are integrated to obtain the final electricity price prediction value. The experimental results demonstrate that the model prediction exhibits an accuracy of up to 93.3%, exhibiting the highest-accuracy compared to other variants of the model. It displays strong generalisation and reliability and is capable of providing effective and intelligent technical support for the power system, power market and its participants.