Application of HIMVO-SVM in short-term load forecasting

Refined load forecasting is very important for daily dispatching planning of power grid. A short-term load forecasting model based on hybrid improved multivariate universe optimization (HIMVO) algorithm and support vector machine (SVM) optimization is proposed in this paper. Firstly, in order to get rid of the shortcomings of the traditional MVO algorithm easy to fall into local optimization, the tent map-based chaotic sequence is used to participate in population initialization. In the updating of the position vector of the MVO algorithm, a non-linear inertia weight reduction strategy is introduced. The differential evolution (DE) algorithm is added to carry out global search, and the HIMVO algorithm is proposed to optimize the parameters of SVM. Finally, the load data of a city in Anhui Province are simulated. The prediction results are compared with GA-SVM and PSO-SVM methods, which proves that HIMVO-SVM method can effectively improve the prediction accuracy.