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Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings
The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a n...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776807/ https://www.ncbi.nlm.nih.gov/pubmed/35058495 http://dx.doi.org/10.1038/s41598-022-04923-7 |
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author | Ngo, Ngoc-Tri Truong, Thi Thu Ha Truong, Ngoc-Son Pham, Anh-Duc Huynh, Nhat-To Pham, Tuan Minh Pham, Vu Hong Son |
author_facet | Ngo, Ngoc-Tri Truong, Thi Thu Ha Truong, Ngoc-Son Pham, Anh-Duc Huynh, Nhat-To Pham, Tuan Minh Pham, Vu Hong Son |
author_sort | Ngo, Ngoc-Tri |
collection | PubMed |
description | The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0–1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management. |
format | Online Article Text |
id | pubmed-8776807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87768072022-01-24 Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings Ngo, Ngoc-Tri Truong, Thi Thu Ha Truong, Ngoc-Son Pham, Anh-Duc Huynh, Nhat-To Pham, Tuan Minh Pham, Vu Hong Son Sci Rep Article The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0–1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management. Nature Publishing Group UK 2022-01-20 /pmc/articles/PMC8776807/ /pubmed/35058495 http://dx.doi.org/10.1038/s41598-022-04923-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ngo, Ngoc-Tri Truong, Thi Thu Ha Truong, Ngoc-Son Pham, Anh-Duc Huynh, Nhat-To Pham, Tuan Minh Pham, Vu Hong Son Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings |
title | Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings |
title_full | Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings |
title_fullStr | Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings |
title_full_unstemmed | Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings |
title_short | Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings |
title_sort | proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776807/ https://www.ncbi.nlm.nih.gov/pubmed/35058495 http://dx.doi.org/10.1038/s41598-022-04923-7 |
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