Research on Medium and Long Term Generation Side Deviation Prediction of New Power Market Based on Multi-Layer LSTM

Abstract

Background: With the large-scale grid connection operation of new or renewable energy and the access of active loads such as electric vehicles and air conditioners, the electric energy trading business in the power market faces problems such as the rapid expansion of the number of market settlement subjects, explosive growth, various subjects responsible for deviation assessment, various electric energy trading methods and so on. Objective: This paper focuses on the medium and long-term generation side power trading in the new power market. Through cause analysis, induction and summary, algorithm design and case analysis, the problem of generation side deviation prediction is solved and power waste is reduced. Methods: This paper puts forward the reasons for the imbalance of medium and long-term power trading in the new power market dominated by new energy, as well as the deviation prediction algorithm based on multi-layer LSTM, which brings the total historical deviation, total planned deviation, total measurement deviation, new energy consumption and other data into the M-LSTM deep learning network for testing in each provincial power market center. Results: We use the neural network prediction algorithm. Compared with a single LSTM, the multi-layer LSTM can better maintain the characteristics of the sample time series and reduce the prediction error. Compared with BPNN、M-BPNN and Cooperative game theory, LSTM has a better memory effect. Conclusion: The experiment shows that the more accurate prediction deviation of this method can better arrange the generation plan, reduce the loss caused by excessive deviation, reduce the "price trampling" of the power market, and ensure the fair and efficient development of the power market.