Training Data Selection for Short Term Load Forecasting

Short term load forecasting (STLF), which aims to predict system load over an internal of one day or one week, plays a crucial role in the control and scheduling operations of a power system. Most existing techniques on short term load forecasting try to improve the performance by selecting ferent prediction models. However, the performance also rely heavily on the quality of training data. This paper focuses on training data selection which is rarely considered in the previous studies. A novel approach is proposed to filter out abnormal data by analyzing historical load curves. Experiments conducted on the real load data show significant improvement over the baseline method using the same data for training. Furthermore, the approach is feasible and thus can be applied in different situations. On one hand, the approach achieves promising results by using only historical load data which is shown in this study. On the other hand, more information can be easily integrated in the approach to select more appropriate training data for further performance improvement.