A Short-term and High-resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization

Authors

Huaiguang Jiang, National Renewable Energy Laboratory
Yingchen Zhang, National Renewable Energy Laboratory
Eduard Muljadi, National Renewable Energy Laboratory
Jun Jason Zhang, University of Denver
Wenzhong David Gao, University of Denver

Publication Date

7-2018

Document Type

Article

Organizational Units

Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering

Keywords

Load forecasting, Load modeling, Forecasting, Optimization, Predictive models, Support vector machines, Training

Abstract

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.

Publication Statement

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Recommended Citation

Jiang, Huaiguang, et al. “A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization.” IEEE Transactions on Smart Grid, vol. 9, no. 4, 2018, pp. 3341–3350. doi: 10.1109/tsg.2016.2628061.