Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

Yechi Zhang, Jianzhou Wang and Haiyan Lu
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Yechi Zhang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Jianzhou Wang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Haiyan Lu: School of Software, Faculty of Engineering and Information Technology, University of Technology, Sydney 2007, Australia

Energies, 2019, vol. 12, issue 10, 1-27

Abstract: Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

Keywords: electric load forecasting; data preprocessing technique; multiobjective optimization algorithm; combined model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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