Uncovering Dominant Features in Short-term Power Load Forecasting Based on Multi-source Feature.

Due to the limitation of data availability, traditional power load forecasting methods focus more on studying the load variation pattern and the influence of only a few factors such as temperature and holidays, which fail to reveal the inner mechanism of load variation. This paper breaks the limitation and collects 80 potential features from astronomy, geography, and society to study the complex nexus between power load variation and influence factors, based on which a short-term power load forecasting method is proposed. Case studies show that, compared with the state-of-the-art methods, the proposed method improves the forecasting accuracy by 33.0% to 34.7%. The forecasting result reveals that geographical features have the most significant impact on improving the load forecasting accuracy, in which temperature is the dominant feature. Astronomical features have more significant influence than social features and features related to the sun play an important role, which are obviously ignored in previous research. Saturday and Monday are the most important social features. Temperature, solar zenith angle, civil twilight duration, and lagged clear sky global horizontal irradiance have a V-shape relationship with power load, indicating that there exist balance points for them. Global horizontal irradiance is negatively related to power load.