Data-Driven Tools for Building Energy Consumption Prediction: A 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 t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/6/2574 |
| _version_ | 1797612090736771072 |
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| author | Razak Olu-Ajayi Hafiz Alaka Hakeem Owolabi Lukman Akanbi Sikiru Ganiyu |
| author_facet | Razak Olu-Ajayi Hafiz Alaka Hakeem Owolabi Lukman Akanbi Sikiru Ganiyu |
| author_sort | Razak Olu-Ajayi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T06:37:32Z |
| format | Article |
| id | doaj.art-199e132e2476432f9a462c5a314890e0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T06:37:32Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-199e132e2476432f9a462c5a314890e02023-11-17T10:48:00ZengMDPI AGEnergies1996-10732023-03-01166257410.3390/en16062574Data-Driven Tools for Building Energy Consumption Prediction: A ReviewRazak Olu-Ajayi0Hafiz Alaka1Hakeem Owolabi2Lukman Akanbi3Sikiru Ganiyu4Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UKBig Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UKFaculty of Business and Law (FBL), University of the West of England, Bristol BS16 1QY, UKFaculty of Business and Law (FBL), University of the West of England, Bristol BS16 1QY, UKBig-DEAL Laboratory, Teesside University, Middlesbrough TS1 3BX, UKThe 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.https://www.mdpi.com/1996-1073/16/6/2574building energy consumption predictiondata driven toolsenergy conservationenergy efficiencyenergy predictionmachine learning |
| spellingShingle | Razak Olu-Ajayi Hafiz Alaka Hakeem Owolabi Lukman Akanbi Sikiru Ganiyu Data-Driven Tools for Building Energy Consumption Prediction: A Review Energies building energy consumption prediction data driven tools energy conservation energy efficiency energy prediction machine learning |
| title | Data-Driven Tools for Building Energy Consumption Prediction: A Review |
| title_full | Data-Driven Tools for Building Energy Consumption Prediction: A Review |
| title_fullStr | Data-Driven Tools for Building Energy Consumption Prediction: A Review |
| title_full_unstemmed | Data-Driven Tools for Building Energy Consumption Prediction: A Review |
| title_short | Data-Driven Tools for Building Energy Consumption Prediction: A Review |
| title_sort | data driven tools for building energy consumption prediction a review |
| topic | building energy consumption prediction data driven tools energy conservation energy efficiency energy prediction machine learning |
| url | https://www.mdpi.com/1996-1073/16/6/2574 |
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