Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network

Yuwen You (), Zhonghua Wang, Zhihao Liu, Chunmei Guo and Bin Yang
Additional contact information
Yuwen You: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
Zhonghua Wang: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
Zhihao Liu: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
Chunmei Guo: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
Bin Yang: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China

Energies, 2024, vol. 17, issue 16, 1-19

Abstract: Cogeneration is an important means for heat supply enterprises to obtain heat, and accurate load prediction is particularly crucial. The heat load of a centralized heat supply system is influenced by various factors such as outdoor meteorological parameters, the building envelope structure, and regulation control, which exhibit a strong coupling and nonlinearity. It is essential to identify the key variables affecting the heat load at different heating stages through data mining techniques and to use deep learning algorithms to precisely regulate the heating system based on load predictions. In this study, a heat station in a northern Chinese city is taken as the subject of research. We apply the Fuzzy Clustering based on Fourier distance (FCBD-FCM) algorithm to transform the factors influencing the long and short-term load prediction of heat supply from the time domain to the frequency domain. This transformation is used to analyze the degree of their impact on load changes and to extract factors with significant influence as the multifeatured input variables for the prediction model. Five neural network models for load prediction are established, namely, Backpropagation (BP), convolutional neural network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and CNN-BiLSTM. These models are compared and analyzed for their performance in long-term, short-term, and ultrashort-term heating load prediction. The findings indicate that the load prediction accuracy is high when multifeatured input variables are based on fuzzy clustering. Furthermore, the CNN-BiLSTM model notably enhances the prediction accuracy and generalization ability compared to other models, with the Mean Absolute Percentage Error (MAPE) averaging within 3%.

Keywords: centralized heating; load forecasting; FCBD-FCM; deep learning; CNN-BiLSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/4190/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/4190/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:16:p:4190-:d:1461615

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().