Power Flow Prediction: A Case in Ningxia Electricity Market

With the further opening of the bidding market in China, the accuracy of electricity price prediction directly affects the operational decisions and profits of power producers. The core factor that affects electricity price is power flow. In the early stage of electric power reform, the data of electricity price was too insufficient to support the forecasting analysis. This paper assists electric power traders to fill in the appropriate amount of electricity during the transaction process by predicting the relevant cross-section power flow. Computational methods are complex and require data of many variables at present. Therefore, this paper uses autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) model to predict the power flow. The prediction error of the model is less than 5%. Furthermore, the conclusion shows that there is no difference between weekdays and weekends, and the power flow is a stationary time series. Based on the result of this research, some decision-making suggestions that can maximize the profit of the manufacturer are given.