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

Razak Olu-Ajayi, Hafiz Alaka, Hakeem Owolabi, Lukman Akanbi, Sikiru Abiodun Ganiyu

Research output: Contribution to journalReview articlepeer-review


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.
Original languageEnglish
Article number2574
Issue number6
Publication statusPublished - 9 Mar 2023


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