多年度データのトレンド処理に基づいた最大電力予測

抄録

This paper presents a regression based daily peak load forecasting method using multiple years data with trend cancellation and trend estimation techniques. Daily peak load heavily depends on temperature in daytime and is influenced by the other weather factors such as humidity. Since a characteristic of the load is varying, peak loads just before a forecasting day are more significant for the forecasting. The regression model can represent relationships between these weather factors and peak loads. However, the forecasting model is sometime not adequate for precise load forecasting. The regression model is well matched with the late data, but the model causes large forecasting errors in transitional seasons because of seasonal change of load characteristics. In order to forecast precisely through a year, a method of using seasonal or whole year data in past years is proposed. In this paper, two kinds of trend data processing techniques are described. The first is trend cancellation. The second is trend estimation. The trend cancellation technique removes annual load growth by means of division or subtraction processes with morning load on the forecasting day. The trend estimation technique estimates the trend between the forecasting year's load and the past year's load by using the variable transformation techniques. Performance of the both techniques verified with simulations on actual load data is also described.