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

Yeming Dai, Xinyu Yang and Mingming Leng

Technological Forecasting and Social Change, 2022, vol. 182, issue C

Abstract: 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.

Keywords: Empirical mode decomposition; Minimal redundancy maximal relevance; Weighted gray relation projection algorithm; Second-order oscillation and repulsion particle swarm optimization; Power load forecasting (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:182:y:2022:i:c:s0040162522003821

DOI: 10.1016/j.techfore.2022.121858

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