One-week-ahead electricity price forecasting using weather forecasts, and its application to arbitrage in the forward market: an empirical study of the Japan Electric Power Exchange

Abstract

Although forecasting one-week-ahead average electricity prices is necessary for decision-making such as evaluating forward contracts, its modeling has not been sufficiently studied. Therefore, to find a suitable forecasting approach, this study constructs and compares multiple parsimonious models using widely published weekly weather forecasts and then applies them to arbitrage trading in the forward market. In particular, we clarify the following empirical results using the data from Japan Electric Power Exchange. First, instead of using forecasted temperature directly as an explanatory variable, the two-step forecasting method using measured temperature as an intermediate variable is more likely to reduce forecast errors. Second, quantile regression has better density forecast accuracy than the generalized autoregressive conditional heteroscedasticity model. Third, the logarithmic conversion for prices tends to improve forecast accuracy. Fourth, one-week-ahead weather forecasts can significantly improve both the price forecast accuracy and the arbitrage profit. The proposed arbitrage strategy can be used by many participants because it can be flexibly changed according to the player’s risk tolerance. In addition, our forecasting/trading method, based on published weather forecasts, has wide applicability in that it can be constructed even in markets where system information is not sufficiently disclosed.