A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; Zhang, Jun Jason; Gao, David Wenzhong

doi:10.1109/TSG.2016.2628061

Title: A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

Journal Article · Fri Nov 18 00:00:00 EST 2016 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/TSG.2016.2628061· OSTI ID:1424580

Jiang, Huaiguang ^[1];

^[1]; Muljadi, Eduard ^[1]; Zhang, Jun Jason ^[2];

^[2]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. of Denver, Denver, CO (United States)

Here, 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 (PSO) 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 the paper.

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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1424580

Report Number(s):: NREL/JA-5D00-68193

Journal Information:: IEEE Transactions on Smart Grid, Vol. 9, Issue 4; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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