Abstract
As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting.
Highlights
With the continuous development of the national economy, the demand for electricity is increasing day by day
Wang and Li [39] proposed a forecasting method based on empirical mode decomposition (EMD) and least squares support vector machine (LSSVM)
The main content and structure of this paper are as follows: the second section introduces the forecasting algorithm—MPSO-LSSVM—using a particle swarm optimization algorithm modified by averaging particle distance and mutation operator to optimize the parameters of the least squares support vector machine
Summary
With the continuous development of the national economy, the demand for electricity is increasing day by day. Li and Huang [4] proposed a short-term power load forecasting method based on a BP neural network. A modified particle swarm optimization algorithm with averaging particle distance and mutation operator is proposed to optimize the parameters of least squares support vector machine. Wang and Li [39] proposed a forecasting method based on empirical mode decomposition (EMD) and least squares support vector machine (LSSVM). A hybrid EMD-GRA-MPSO-LSSVM model with high accuracy for short-term load forecasting is proposed. The main content and structure of this paper are as follows: the second section introduces the forecasting algorithm—MPSO-LSSVM—using a particle swarm optimization algorithm modified by averaging particle distance and mutation operator to optimize the parameters of the least squares support vector machine.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have