Research on Power System Ultimate Load Prediction Based on Parallel Particle Swarm Optimization Algorithm
There are differences between the predicted results of existing prediction methods and the actual values,and the average error results are relatively large.Therefore,a parallel particle swarm optimization algorithm based extreme load prediction for power systems is studied.Perform error data processing,normalization,and standardization on the original load series data,so that the data can be scaled to a certain proportion and stored in a specific interval,resulting in dimensionless data.Using parallel particle swarm optimization algorithm for load forecasting,adjusting inertia weights to increase global and local search capabilities.Calculate the fitness value and compare it with the optimal fitness value,and replace it if the position is better.When the load prediction results meet the pre-set threshold,short-term load forecasting is completed.The experimental results indicate that the model in the experimental group performs better and its predicted results are more consistent with the actual values.The average error results of the calculation are all between 0.32 and 0.36 MW,within the specified error range,which can accurately predict the ultimate load of the power system and have a more ideal prediction result.
万方数据
维普
