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• W4313368260 endingPage "126561" @default.
• W4313368260 startingPage "126561" @default.
• W4313368260 abstract "Accurate and stable power load prediction is useful for electric power enterprises. However, accurate and stable power load prediction becomes very difficult. In order to improve prediction accuracy and stability, an improved hybrid model based on variational mode decomposition (VMD) optimized by the cuckoo search algorithm (CSA), seasonal autoregressive integrated moving average (SARIMA) and deep belief network (DBN) is put foreword for short term power load prediction. First, the original power load is decomposed into several regular and random sub-series by VMD-CSA. Second, the regular sub-series is predicted by SARIMA, and the random sub-series is predicted by DBN. Third, the final prediction result is the sum of each sub-series prediction result. The validity of the proposed model is verified by using power load from three different markets. Experimental results show that the proposed model has more accurate and stable results." @default.
• W4313368260 created "2023-01-06" @default.
• W4313368260 creator A5005944481 @default.
• W4313368260 creator A5034653760 @default.
• W4313368260 creator A5083711673 @default.
• W4313368260 creator A5085617732 @default.
• W4313368260 date "2023-04-01" @default.
• W4313368260 modified "2023-10-18" @default.
• W4313368260 title "An improved hybrid model for short term power load prediction" @default.
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• W4313368260 doi "https://doi.org/10.1016/j.energy.2022.126561" @default.
• W4313368260 hasPublicationYear "2023" @default.
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