Short-term Load Forecasting Model for Microgrid Based on HSA-SVM

Han Aoyang,An Shuhuai,Yu Litao,Zhang Zhisheng,J Heled,A Yuan

doi:10.1051/matecconf/201817301007

Han Aoyang, An Shuhuai + Show 4 more

Open Access

https://doi.org/10.1051/matecconf/201817301007

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Abstract

Short-term load forecasting for microgrid is the basis of the research on scheduling techniques of microgrid. Accurate load forecasting for microgrid will provide the necessary basis for cooperative optimization scheduling. Short-term loadforecasting model for microgrid based on support vector machine(SVM) is constructed in this paper. The harmony search optimization algorithm(HSA) is used to optimize the parameters of the SVM model, because it has the advantages of fast convergence speed and better optimization ability. Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-SVM can effectively improve the prediction accuracy.

Highlights

Smart city is the deep expansion and integrated application of information technology, and it is an important part of the development of the new global strategic industries
Accurate load forecasting for microgrid will provide the necessary basis for cooperative optimization scheduling [1,2]
Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-support vector machine (SVM) can effectively improve the prediction accuracy

Summary

Introduction

Smart city is the deep expansion and integrated application of information technology, and it is an important part of the development of the new global strategic industries. For the neural network model, the neural network based on BP algorithm can describe the nonlinear relationship between load and impact factor well and is widely used in short-term load forecasting. This kind of model has some shortcomings such as slow convergence, easy falling into the local minimum point and so on[8]. SVM is a statistical learning theory for classification and regression It is a new type of machine learning algorithm based on the principle of structural risk minimization. Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-SVM can effectively improve the prediction accuracy

Support vector machine

Optimization for parameters of SVM based on improved HSA

Quantitative treatment of load and influence factors

Findings

Conclusion