Thermal load prediction and operation optimization of office building with a zone-level artificial neural network and rule-based control

Jingfan Hu, Wandong Zheng, Sirui Zhang, Hao Li, Zijian Liu, Guo Zhang and Xu Yang

Applied Energy, 2021, vol. 300, issue C, No S0306261921008229

Abstract: Precise and quick thermal load prediction for buildings is imperative in realizing the flexibility of building energy systems. Operation optimization based on the prediction results can effectively mitigate the energy consumption and operation cost. Here, an artificial neural network (ANN) model is built to predict the load demand and energy consumption of an office building. Thermal zones division is considered to improve the prediction accuracy. Measured data are collected to establish and validate the ANN model. The total number of samples for training, validation and testing are 13674, 3978 and 3977, respectively. A rule-based control optimization model is introduced and combined with ANN model to optimize the operation of heating, ventilation and air-conditioning system. Building energy flexibility is activated by optimization model in a time-of-use electricity price scenario. The results indicate that ANN model has high precision and the coefficient of variation of root mean squared errors corresponding to the load demand prediction and energy consumption prediction are 10.76% and 15.59%, respectively. Furthermore, the optimization results can reduce the operation cost by 39.22% and 44.41% in the heating and cooling season, respectively.

Keywords: Artificial neural network; Operation optimization; Thermal zones division; Building energy flexibility; Thermal storage tank (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921008229
Full text for ScienceDirect subscribers only

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:eee:appene:v:300:y:2021:i:c:s0306261921008229

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2021.117429

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().