Prophet-EEMD-LSTM based method for predicting energy consumption in the paint workshop

• Published in: Applied soft computing 2023-08, Vol.143, p.110447, Article 110447
• Main Authors: Lu, Yingkang, Sheng, Buyun, Fu, Gaocai, Luo, Ruiping, Chen, Geng, Huang, Yuzhe
• Format: Article
• Language: English
• Subjects: Energy consumption prediction; Ensemble empirical mode decomposition; Long short-term memory networks; Periodic decomposition; Prophet
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110447

Energy conservation and preventive maintenance of equipment require the ability to accurately predict future trends in shop floor power consumption to keep track of equipment operation and abnormalities. Due to the non-linearity and uncertainty of the workshop electrical energy consumption data, it is very difficult to establish an accurate energy consumption prediction model. For the above problems, an energy consumption prediction model based on Prophet-EEMD-LSTM was proposed. To identify the period features in the energy consumption time series data, the Prophet algorithm was introduced to identify and extract the period and trend features of the energy consumption data. Then for the feature of the uncertainty of the remaining data, this paper used the ensemble empirical mode decomposition (EEMD) method to decompose the data to form the components with specific modalities. Finally, all the extracted feature data were put into the input layer of the Long short-term memory (LSTM), and the final energy consumption values were predicted using the excellent prediction performance of the LSTM on time-series data. The experimental results showed that the model proposed in this paper has high prediction performance for energy consumption data. •Energy consumption prediction models guide energy saving and predictive maintenance in paint workshop.•The model uses only energy consumption data for predictions, eliminating the need for other data.•The complexity of the data is reduced by Prophet and EEMD, and the LSTM captures the temporal characteristics of the data.•The Prophet-EEMD-LSTM model shows higher performance with an average R2 value of 0.94006.