Data-Driven Tools for Building Energy Consumption Prediction: A Review

Razak Olu-Ajayi (), Hafiz Alaka, Hakeem Owolabi, Lukman Akanbi and Sikiru Ganiyu
Additional contact information
Razak Olu-Ajayi: Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK
Hafiz Alaka: Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK
Hakeem Owolabi: Faculty of Business and Law (FBL), University of the West of England, Bristol BS16 1QY, UK
Lukman Akanbi: Faculty of Business and Law (FBL), University of the West of England, Bristol BS16 1QY, UK
Sikiru Ganiyu: Big-DEAL Laboratory, Teesside University, Middlesbrough TS1 3BX, UK

Energies, 2023, vol. 16, issue 6, 1-20

Abstract: The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the inappropriate application of data-driven tools for energy consumption prediction in the wrong conditions. For example, employing a data-driven tool to develop a model using a small sample size, despite the recognition of the tool for producing good results in large data conditions. This study delivers a review of 63 studies with a precise focus on evaluating the performance of data-driven tools based on certain conditions; i.e., data properties, the type of energy considered, and the type of building explored. This review identifies gaps in research and proposes future directions in the field of data-driven building energy consumption prediction. Based on the studies reviewed, the outcome of the evaluation of the data-driven tools performance shows that Support Vector Machine (SVM) produced better performance than other data-driven tools in the majority of the review studies. SVM, Artificial Neural Network (ANN), and Random Forest (RF) produced better performances in more studies than statistical tools such as Linear Regression (LR) and Autoregressive Integrated Moving Average (ARIMA). However, it is deduced that none of the reviewed tools are predominantly better than the other tools in all conditions. It is clear that data-driven tools have their strengths and weaknesses, and tend to elicit distinctive results in different conditions. Hence, this study provides a proposed guideline for the selection tool based on strengths and weaknesses in different conditions.

Keywords: building energy consumption prediction; data driven tools; energy conservation; energy efficiency; energy prediction; machine learning (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 (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/6/2574/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/6/2574/ (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:6:p:2574-:d:1091853

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 ().