A short-term load forecasting method for main network considering multi-scenario operation

Conventional short-term load forecasting methods for main network, which are mainly based on load change feature extraction, are affected by the complexity of forecasting and cannot adapt to multiple operation scenarios, which affects the accuracy of the final forecast. Therefore, a short-term load forecasting method for main grid considering multiple operation scenarios is designed. The uncertainty probability density of main network short-term load prediction is determined, and according to the distribution characteristics of load, a suitable probability density function is selected for fitting to ensure the accuracy of subsequent load prediction. Based on multi-scenario operation, the model in this paper is constructed, and different operation scenarios are defined according to historical load data, weather conditions, economic activities and other factors, so as to arrive at more accurate load forecasting values. The short-term load forecasting model is optimized in parallel, and the distributed computing framework is used to process the load data in parallel to meet the forecasting performance requirements.