Automatic Prediction Method of Short-Term Load in Electric Power System Based on Improved Particle Swarm Algorithm
To solve the problem of high misjudgment rate in multi segment short-term load forecasting caused by strong chaotic characteristics and noise impact of load data in the power system,an improved particle swarm algorithm is introduced to propose a new automatic short-term load forecasting method for the new power system.Firstly,determine the source of power load data and collect load data.Secondly,the collected load data is horizontally processed to remove noise data and correct errors in power load data,providing strong data support for subsequent short-term load forecasting.Finally,using the improved particle swarm algorithm,the short-term load forecasting problem is transformed into a multi-objective optimization problem.Through iteration and optimization,the optimal solution is found to obtain the future short-term load forecasting results.The results show that after applying this method,the average absolute percentage error of power load prediction at each time point does not exceed 0.5%,and the predicted load value is closer to the actual value.The prediction misjudgment rate has been significantly improved.
improved particle swarm algorithmnew power systemshort-term load
NETL
NSTL
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