Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

Binglin Li (), Yong Shao, Yufeng Lian, Pai Li and Qiang Lei
Additional contact information
Binglin Li: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Yong Shao: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Yufeng Lian: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Pai Li: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Qiang Lei: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China

Energies, 2023, vol. 16, issue 17, 1-14

Abstract: With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson’s correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R 2 ) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal.

Keywords: Bayesian optimization; load forecasting; recurrent neural network; time series (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/17/6234/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/17/6234/ (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:16:y:2023:i:17:p:6234-:d:1226826

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 ().