Fusion Forecasting Algorithm for Short-Term Load in Power System

Tao Yu, Ye Wang, Yuchong Zhao, Gang Luo and Shihong Yue ()
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Tao Yu: CCCC Tianjin Waterway Bureau Co., Ltd., Tianjin 300202, China
Ye Wang: CCCC Tianjin Waterway Bureau Co., Ltd., Tianjin 300202, China
Yuchong Zhao: CCCC Tianjin Waterway Bureau Co., Ltd., Tianjin 300202, China
Gang Luo: CCCC Tianjin Waterway Bureau Co., Ltd., Tianjin 300202, China
Shihong Yue: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2024, vol. 17, issue 20, 1-12

Abstract: Short-term load forecasting plays an important role in power system scheduling, optimization, and maintenance, but no existing typical method can consistently maintain high prediction accuracy. Hence, fusing different complementary methods is increasingly focused on. To improve forecasting accuracy and stability, these features that affect the short-term power system are firstly extracted as prior knowledge, and the advantages and disadvantages of existing methods are analyzed. Then, three typically methods are used for short-term power load forecasting, and their interaction and complementarity are studied. Finally, the Choquet integral (CI) is used to fuse the three existing complementarity methods. Different from other fusion methods, the CI can fully utilize the interactions and complementarity among different methods to achieve consistent forecasting results, and reduce the disadvantages of a single forecasting method. Essentially, a CI with n inputs is equivalent to n ! constrained feedforward neural networks, leading to a strong generalization ability in the load prediction process. Consequently, the CI-based method provides an effective way for the fusion forecasting of short-term load in power systems.

Keywords: short-term load forecasting; time series; information fusion; Choquet integral (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: 2024
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