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Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network
Highlights A new hybrid model, namely CEEMDAN-SE-LSTM is proposed. The model allows for more accurate forecasting of electrical loads. The model is significantly better than models like EEMD-LSTM, CEEMDAN-LSTM, etc.
Abstract Ultra-short-term power load forecasting refers to the use of load and weather information from the prior few hours to forecast the load for the next hour, which is very important for power dispatch and the power spot market establishment. Based on time series decomposition-reconstruction modeling and neural network forecasting, this study constructed a CEEMDAN-1 May 13, 2014, was chosen as the starting time because the meteorological data (e.g., temperature, rainfall, wind speed, etc.) obtained by the authors for Changsha City started from that time; and to highlight the cyclical nature of the electricity load demand, given the lack of meteorological data afterApril 2018, May 13, 2017, was chosen as the cut-off date in this paper. However, we believe that the starting and ending time does not affect the idea and method of model construction in this paper.SE-LSTM model and used it to forecast the ultra-short-term electricity load in Changsha, China, considering meteorological and holiday factors. The article first decomposed the power load data from May 13, 2014, to May 13, 2017, at 24 time points per day for three years to obtain six component series, and then reconstructed them into a two-component series based on the sample entropy analysis to reflect the fluctuation and trend characteristics of the power load. Then, the LSTM neural network model was used to predict and superimpose the reconstructed component series to obtain the final prediction results. It was found that the RMSE, MAE, and MAPE of the CEEMDAN-SE-LSTM model were 62.102, 47.490, and 1.649 %, respectively, which were significantly better than those of the ARMA, LSTM single-prediction, EEMD-LSTM, and CEEMDAN-LSTM models. This study greatly improves the accuracy of ultra-short-term power-load forecasting, provides support for ultra-short-term power dispatching in Changsha, and provides a reference for other cities to develop short-term and ultra-short-term power load forecasting models.
Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network
Highlights A new hybrid model, namely CEEMDAN-SE-LSTM is proposed. The model allows for more accurate forecasting of electrical loads. The model is significantly better than models like EEMD-LSTM, CEEMDAN-LSTM, etc.
Abstract Ultra-short-term power load forecasting refers to the use of load and weather information from the prior few hours to forecast the load for the next hour, which is very important for power dispatch and the power spot market establishment. Based on time series decomposition-reconstruction modeling and neural network forecasting, this study constructed a CEEMDAN-1 May 13, 2014, was chosen as the starting time because the meteorological data (e.g., temperature, rainfall, wind speed, etc.) obtained by the authors for Changsha City started from that time; and to highlight the cyclical nature of the electricity load demand, given the lack of meteorological data afterApril 2018, May 13, 2017, was chosen as the cut-off date in this paper. However, we believe that the starting and ending time does not affect the idea and method of model construction in this paper.SE-LSTM model and used it to forecast the ultra-short-term electricity load in Changsha, China, considering meteorological and holiday factors. The article first decomposed the power load data from May 13, 2014, to May 13, 2017, at 24 time points per day for three years to obtain six component series, and then reconstructed them into a two-component series based on the sample entropy analysis to reflect the fluctuation and trend characteristics of the power load. Then, the LSTM neural network model was used to predict and superimpose the reconstructed component series to obtain the final prediction results. It was found that the RMSE, MAE, and MAPE of the CEEMDAN-SE-LSTM model were 62.102, 47.490, and 1.649 %, respectively, which were significantly better than those of the ARMA, LSTM single-prediction, EEMD-LSTM, and CEEMDAN-LSTM models. This study greatly improves the accuracy of ultra-short-term power-load forecasting, provides support for ultra-short-term power dispatching in Changsha, and provides a reference for other cities to develop short-term and ultra-short-term power load forecasting models.
Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network
Li, Ke (author) / Huang, Wei (author) / Hu, Gaoyuan (author) / Li, Jiao (author)
Energy and Buildings ; 279
2022-11-13
Article (Journal)
Electronic Resource
English
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