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Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms
Abstract This paper presents an ensemble algorithm based on a new load decomposition method to forecast short-term metropolitan-scale electric load. In this method, a decision tree for hourly seasonal attributes and a weighted average method for daily seasonal attributes are first applied to divide seasons into a completely different way. Then, the load of transition seasons is chosen as a basic component according to power load characteristics, and the differences between total load and the basic component are extracted as the weather-sensitive component. Finally, a time-series method is selected to forecast the basic component and SVM (Support Vector Machine) to the weather-sensitive component. This paper takes the annual electricity load of Shanghai as a case study to verify this ensemble method. The results show that compared with the traditional model based on overall daily load and other load decomposition methods—EMD (Empirical Mode Decomposition) and WT (Wavelet Transform), this ensemble model reduces the error from 3 to 5% to lower than 2% when forecasting the power load of workdays, and for non-work days, the error is decreased from 4 to 5% to lower than 4%.
Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms
Abstract This paper presents an ensemble algorithm based on a new load decomposition method to forecast short-term metropolitan-scale electric load. In this method, a decision tree for hourly seasonal attributes and a weighted average method for daily seasonal attributes are first applied to divide seasons into a completely different way. Then, the load of transition seasons is chosen as a basic component according to power load characteristics, and the differences between total load and the basic component are extracted as the weather-sensitive component. Finally, a time-series method is selected to forecast the basic component and SVM (Support Vector Machine) to the weather-sensitive component. This paper takes the annual electricity load of Shanghai as a case study to verify this ensemble method. The results show that compared with the traditional model based on overall daily load and other load decomposition methods—EMD (Empirical Mode Decomposition) and WT (Wavelet Transform), this ensemble model reduces the error from 3 to 5% to lower than 2% when forecasting the power load of workdays, and for non-work days, the error is decreased from 4 to 5% to lower than 4%.
Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms
Chu, Yiyi (author) / Xu, Peng (author) / Li, Mengxi (author) / Chen, Zhe (author) / Chen, Zhibo (author) / Chen, Yongbao (author) / Li, Weilin (author)
Energy and Buildings ; 225
2020-07-24
Article (Journal)
Electronic Resource
English
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