An energy consumption prediction of large public buildings based on data-driven model

Yongbing Guan and Yebo Fang

International Journal of Global Energy Issues, 2023, vol. 45, issue 3, 207-219

Abstract: Because the traditional energy consumption prediction method of large public buildings has the problems of large prediction error and long prediction time, an energy consumption prediction of large public buildings based on data-driven model is proposed. The process includes to build the energy consumption model of large public buildings through data driving, collect energy consumption data such as equipment capacity, load grade and equipment failure rate, pre-process the energy consumption data, take the pre-processed energy consumption data as training samples, input it into BP neural network for training, optimise BP neural network by genetic algorithm and build the energy consumption prediction model of large public buildings, and get the prediction results. The simulation results show that the energy consumption prediction method of large public buildings based on data-driven model has short time and good prediction effect.

Keywords: data-driven model; large public buildings; energy consumption prediction; BP neural network; genetic algorithm. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=130682 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijgeni:v:45:y:2023:i:3:p:207-219

Access Statistics for this article

More articles in International Journal of Global Energy Issues from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().