Bayesian optimization in a support vector regression model for short-term electricity load forecasting
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Abstract
Purpose: Support vector regression (SVR) has long been known as a great tool in forecasting. SVR combined with evolutionary algorithms have been used in many remarkable applications. Bayesian optimization (BO) has the ability to find good points in a search space without many function evaluations. This paper presents short-term load forecasting (STLF) in Thailand using SVR with Bayesian optimization (BO). The purpose of this paper is to improve forecasting accuracy by optimizing the hyperparameters of SVR.
Design/methodology/approach: The Electricity Generating Authority of Thailand (EGAT) provides 30 minute load data for Bangkok and the metropolitan region. The data from August 2015 to July 2017 is used for training and testing. Mean absolute percentage error (MAPE) and tracking signal (TS) are used to measure the performance of the proposed model. The hyperparameters of SVR are optimized using three algorithms, the genetic algorithm (GA), particle swarm optimization (PSO), and Bayesian optimization (BO). Findings: By comparing the MAPE results, the SVR-BO outperforms the other two algorithms.
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