Short-term power load forecasting in distribution networks considering human comfort level

The growth of power demand and the increase of new energy penetration have resulted in a heightened necessity for the precision of short-term power load forecasting in distribution networks. The majority of current research on short-term load forecasting is focused on the improvement of algorithms, with relatively limited attention paid to meteorological factors. Furthermore, research in this area typically focuses on a single meteorological factor, namely, temperature, and does not sufficiently address the processing of meteorological data features. In light of the aforementioned considerations, this paper puts forth a human comfort model founded upon the ordering relationship analysis method and the entropy weight method. Furthermore, it employs the XGBoost algorithm to construct a short-term load forecasting model, utilizing the human comfort score and historical load data as inputs for forecasting the load. This approach is intended to enhance the precision of the load forecasting. The experimental results demonstrate that the proposed prediction model exhibits superior performance in short-term load forecasting, achieving a significantly higher level of accuracy than the baseline model. This model offers a notable advancement in practical forecasting applications.