Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation
Matheus Soares Geraldi and
Enedir Ghisi
Energy, 2022, vol. 244, issue PB
Abstract:
Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods.
Keywords: Energy benchmarking; Building performance analysis; Building energy performance; Energy efficiency; School buildings (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222000640
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:energy:v:244:y:2022:i:pb:s0360544222000640
DOI: 10.1016/j.energy.2022.123161
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().