Short-Term Load Forecasting of Power System Considering Seasonal Variation Impact on Load Characteristic
With the increasing complexity of microgrid electrical equipment,environmental and economic issues are becoming more prominent.Short-term load forecasting is crucial for the refined regional dispatching.Current load forecasting methods often lack characterization of seasonal variation factors across different regions,resulting in lower prediction accuracy.Therefore,this paper proposes a short-term load forecasting method based on ICEEMDAN-RIME-BiGRU that considers seasonal differences.Firstly,the ICEEMDAN method is used to de-compose the electrical load into seasonal components.Secondly,integrating the soft frost search strategy,hard frost puncture mechanism,and forward greedy selection mechanism of the RIME algorithm,the method learns the characteristics of load components in different seasons to optimize the parameters of the BiGRU model.These feature components are then input into the network model,and the results are aggregated to obtain the time series forecast values.Finally,a case study using load data from a microgrid in a specific region is conducted.The re-sults demonstrate that compared to three other typical correlation forecasting methods,the proposed method effec-tively characterizes the impact of seasonal differences on load,thereby improving load forecasting accuracy.
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