Research on spot market price forecasting method considering the electricity‐purchase gain for demand side

Abstract

The clearing price in electricity spot market is an important reference guiding market participants to purchase energy. Current electricity price forecasting methods mainly focus on improving numerical accuracy, and the need to optimize economic benefits is ignored. However, higher numerical precision sometimes leads to lower electricity‐purchase gain. To deal with that, this paper proposes a price forecasting method that optimizes economic benefits together with numerical accuracies. A revenue‐optimizing term evaluating the relationship between the predicted price and the cost reference price is introduced to the loss function of the prosumers’ forecasting model. A sequence comparison neural network structure is proposed and added to consumers’ model, so the forecasting model is trained by also considering price trend. By co‐optimizing numerical precision and electricity‐purchase gain, the prediction is more conducive to reducing the cost of purchasing power. Price data in actual electricity market are used to verify the feasibility and improvement of the proposed method.