A closed-loop data-fusion framework for air conditioning load prediction based on LBF. (November 2022)

• Record Type: Journal Article
• Authors: He, Ning; Liu, Liqiang; Qian, Cheng; Zhang, Lijun; Yang, Ziqi; Li, Shang
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 7724
• Page End: 7734
• Publication Date: 2022-11
• Subjects: Air conditioning load prediction -- Long short-term memory -- Back propagation neural network -- Particle filter -- Locally weighted scatterplot smoothing; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.05.289
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Accurate air conditioning load prediction is a key component of intelligent building management system for ensuring energy saving and safe operation of air conditioning system. In order to improve the prediction accuracy, a particle filter (PF) load prediction fusion estimation method based on long short-term memory (LSTM) and back propagation neural network (BP) is proposed. Firstly, spearman correlation analysis is used to select the influencing factors with high correlation as feature input. Aiming at the problem that the original signal is easy to be disturbed by noise and the data features are not obvious, locally weighted scatterplot smoothing (LOWESS) method is used to denoise the data to improve the data quality for further accurate prediction. Secondly, the data-driven air conditioning load state-space representation is established, which takes air conditioning load as the state variable and takes the load features collected by the sensor in real-time as the input variables. Thirdly, combined with the space representation method of air conditioning load based on LSTM-BP, PF is introduced to estimate the air conditioning load by using the fusion model. Meanwhile, the output load value of BP is fed back to the fusion model as the observation value to update the state-space representation of air conditioning load. Finally, two practical cases are used to verify the effectiveness of the method. The results indicate that the proposed method can provide more accurate and robust air conditioning load prediction.