Introduction  Accurate and efficient short-term electricity load forecasting is a prerequisite for ensuring the safe and reliable operation of power system, and it is also the basis for the rational arrangement of power generation plans in the power grid. Therefore, studying the relationship between meteorology and electricity load is of great significance for load forecasting.

  Method  Based on the electricity load data at 15 min intervals during the period between January 1 of 2013 and December 31 of 2021 provided by the State Grid Hebei Electric Power Co., Ltd. as well as the corresponding meteorological observation data of Shijiazhuang station, this paper analyzed the temporal variation characteristics of daily peak electricity load in Shijiazhuang, and in particular, the meteorological conditions corresponding to the samples with a daily peak electricity load that was 10% higher than that of the previous day were analyzed. The Spearman's rank correlation method was used to analyze the correlation between daily peak electricity load in Shijiazhuang and the meteorological factors of the previous day, and significantly correlated meteorological factors were identified. The response curves of the significantly correlated meteorological factors to the next day's peak electricity load were drawn using the smooth curve fitting method, and the analysis revealed the changing trend of daily peak electricity load with the variations of meteorological factors, as well as the response thresholds. For different threshold ranges, the relative risk of meteorological factors to the changes of the daily peak electricity load was calculated based on the Poisson distribution. On this basis, the variation magnitudes of daily peak electricity load caused by per unit change in each meteorological factor within different threshold ranges in Shijiazhuang were calculated, that is, the quantitative impacts of the changes in different meteorological factors on the variation of daily peak electricity load were revealed.

  Result  Taking temperature as an example, when the daily average, maximum and minimum temperatures are higher (lower) than the thresholds, the relative risk to the next day′s peak electricity load increases (decreases) by 2.25% (0.62%), 1.92% (0.57%) and 2.07% (0.60%) respectively for every 1 °C increase in temperature.

  Conclusion  Based on the relative risk of different meteorological factors to daily peak electricity load in Shijiazhuang, a method for predicting the next day′s peak electricity load is proposed. The test performed using the daily electricity load and meteorological data of Shijiazhuang in 2022 reveals that the prediction effect can meet the needs of daily electricity meteorological service.