A weighted plain Bayesian algorithm-based method for automatic scheduling of short-term load resources in power grids

Abstract

Load forecasting is the use of historical data to build a model based on the operating characteristics of the power system, economic conditions, social conditions and natural conditions, and the use of this model to make reliable estimates of the trend of load changes in future periods. Load management is of great importance to the development of many tasks in the power system. Not only does it require comprehensive consideration of many aspects of power dispatch control, operation planning and marketing operations, but it also requires scientific forecasting of power loads in conjunction with actual practice. Short-term load forecasting is an important basis for the power supply company's scheduling plan, helping the staff to monitor the working status of the power system in real time, and is of great significance to the power supply company in reducing costs and the power plant in making power generation plans. However, the load forecasting models established by current research workers do not meet the more stringent forecasting requirements of power supply companies in terms of both accuracy and stability. The main objective of this paper is to investigate the automatic scheduling of short-term load resources for power grids based on a weighted plain Bayesian algorithm. Based on the periodicity and regularity of the historical load, this paper divides the fluctuating intervals of the sequence according to the trend of load changes, extracts the characteristics of the fluctuating intervals to represent the load intervals as electricity consumption patterns and performs density clustering. Based on the clustering results, similar load sequences are selected from the aggregated clusters to determine the reference sequences, and then a load forecasting model is established for load forecasting by combining time series and regression ideas to achieve ultra-short-term load forecasting. The test results show that the method has a good forecasting effect and provides a forecasting method based on electricity consumption patterns for power management departments.