モデルの自己組織化による1日先電力負荷予測

抄録

Load forecasting is a basis for power system planning and operation aiming at high reliability and low cost in electrical energy supply. Accurate load forecasts need description of relationship between the load and its determining factors. However there are various factors such as weather conditions and economic situations that may influence the load. It is not easy to discuss general methodology to choose essential factors and represent the relationship exactly. Thus most of the existing methods concentrate their targets on restricted situations or resort to empirical and subjective knowledge.
In the paper a method is proposed for one-day-ahead load forecasting, which is applicable to general situations without empirical knowledge. It is a self-organizing method for making a load forecasting model in terms of the determining factors. There is no need for knowing definitely which are necessary factors for forecasting. A simple initial structure of the model is given first, then newly measured data are fed to the modeling process everyday, and a suitable model grows up through the daily forecasting. Even after a suitable structure is derived, the self-organization process continues searching for more suitable one. This is useful and important feature since the load has a time varying character.
The method is applied to the daily peak and bottom load. The results show the effectiveness of the method: the self-organized model structures agree in some parts with expected structures from empirical knowledge; and the mean error over one year is around 1.7_??_2.1%.