  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.
