Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings

Kadir Amasyali and Nora El-Gohary

Renewable and Sustainable Energy Reviews, 2021, vol. 142, issue C

Abstract: Building energy consumption prediction plays a key role in energy-efficiency decision making. With the advancement in data analytics, a number of machine learning-based building energy consumption prediction models have been developed in recent years. However, existing prediction models do not sufficiently take occupant behavior into account. Towards addressing this gap, this paper presents a machine-learning approach for predicting building energy consumption in an occupant-behavior-sensitive manner. In this approach, a model learns from a large set of energy-use cases that were modelled and simulated in EnergyPlus. The machine-learning prediction model was trained using a large dataset that includes 3-month hourly data for 5760 energy-use cases representing different combinations of building characteristics, outdoor weather conditions, and occupant behaviors. In developing the model, four machine-learning algorithms were tested and compared in terms of their prediction accuracy and computational efficiency: classification and regression trees (CART), ensemble bagging trees (EBT), artificial neural networks (ANN), and deep neural networks (DNN). The simulation results demonstrated the high impact of the variables considered in this study. For example, the highest energy-consuming case consumed over 3432 times more energy than the lowest-consuming case. Occupant behavior made a difference up to over 7 times in energy consumption. The DNN model with four hidden layers achieved 2.97% coefficient of variation (CV). Such high performance shows the potential of the proposed approach. The approach could help better understand the impact of occupant behavior on building energy consumption and identify opportunities for behavioral energy-saving measures.

Keywords: Building energy prediction; Occupant behavior; Machine learning; Deep learning; Ensemble algorithms; Building performance simulation; EnergyPlus (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032121000113
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:rensus:v:142:y:2021:i:c:s1364032121000113

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2021.110714

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().