Computer-Aided Load Forecasting and Optimal Dispatching Algorithm for Power Systems

In order to solve the problems of low load forecasting accuracy and low operating efficiency caused by strong load fluctuation and rapid changes in electricity demand in the power system, this paper introduces a computer-aided load forecasting and optimization scheduling algorithm to improve the overall operating efficiency of the system and reduce the risk of unbalanced power supply. Firstly, big data analysis technology is used to clean and extract features of historical load data. Then, a load forecasting model based on multivariate time series is constructed by combining support vector machine (SVM) and long short-term memory network (LSTM) to capture the nonlinear characteristics of load changes and improve the accuracy of prediction. Secondly, this paper adopts a hybrid optimization strategy of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to globally search for the optimal scheduling solution and achieve rapid convergence through local fine-tuning. Among them, the SVM-LSTM model has the highest accuracy with an error of 10%. The GA-PSO algorithm performs best, with energy consumption as low as 900 kWh and calculation time of only 30 seconds. The computer-aided power system load forecasting and optimal dispatch algorithm proposed in this paper can operate stably in complex power system environments.