Extreme weather load forecasting method for power systems based on extreme gradient lifting algorithm

In order to reduce power load forecasting errors and ensure stable operation of power systems, an extreme weather load forecasting method based on the extreme gradient boosting algorithm is proposed. We analyze the composition of power system load. Extreme weather factors are considered the main factors affecting the load characteristics of the power system from the perspective of weather factors, and the correlation between meteorological factors and power load is analyzed. The Box–Jenkins method is used to construct the power system load time series based on the analysis results. The extreme gradient boosting algorithm is employed for load prediction by analyzing load time-series data along with meteorological factors, thereby enhancing the precision of load forecasting. The experimental results demonstrate that the proposed method achieves a maximum average absolute error of just 1.54 and a root mean square error of only 3.31. The difference between the predicted values and the actual load values is minimal, with the largest deviation being a mere 0.7 MW. Moreover, under extreme weather scenarios, the relative error of the predictions remains below 5.0%. These results comprehensively attest to the excellent predictive capability of the proposed approach.