Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence

Dai Y.; Yang X.; Leng M.

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Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence

机译：Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence

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? 2022 Elsevier Inc.An accurate power load prediction in smart grid plays an important role in maintaining the balance between power supply and demand and thus ensuring the safe and stable operation of power system. In this paper we develop a hybrid power load prediction method, which involves three main steps: data decomposition with the empirical mode decomposition method, data processes with the minimal redundancy maximal relevance method and the weighted gray relationship projection algorithm, and support vector machine prediction, whose parameters are optimized through the particle swarm optimization algorithm with a second-order oscillation and repulsive force factor. Moreover, we predict the power load with our hybrid forecasting method based on the real dataset from the electricity market in Singapore, and also compare our prediction results with those by using other forecasting methods. Our comparison results show that our novel hybrid method possesses a high accuracy in both the level and directional predictions.

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《Technological forecasting and social change》 |2022年第9期|1-11|共11页
作者
Dai Y.; Yang X.; Leng M.;
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作者单位

School of Business Qingdao University;

Faculty of Business Lingnan University;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
Empirical mode decomposition; Minimal redundancy maximal relevance; Power load forecasting; Second-order oscillation and repulsion particle swarm optimization; Weighted gray relation projection algorithm;

Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence

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