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An Ensemble Machine Learning Model for Enhancing the Prediction Accuracy of Energy Consumption in Buildings

التفاصيل البيبلوغرافية
العنوان: An Ensemble Machine Learning Model for Enhancing the Prediction Accuracy of Energy Consumption in Buildings
المؤلفون: Thi Thu Ha Truong, Ngoc-Son Truong, Anh-Duc Pham, Nhat-To Huynh, Tuan Minh Pham, Ngoc-Tri Ngo
المصدر: Arabian Journal for Science and Engineering. 47:4105-4117
بيانات النشر: Springer Science and Business Media LLC, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Multidisciplinary, Artificial neural network, business.industry, Computer science, Generalization, 010102 general mathematics, Energy consumption, Energy planning, Machine learning, computer.software_genre, 01 natural sciences, Ensemble learning, Support vector machine, Mean absolute percentage error, Artificial intelligence, 0101 mathematics, business, Baseline (configuration management), computer
الوصف: Predicting building energy use is necessary for energy planning, management, and conservation. It is difficult to achieve accurate prediction results due to the inherent complexity of building thermal characteristics and occupant behavior. Machine learning has been recently applied for predicting energy consumption. Improving its predictive accuracy and generalization ability is essential. Therefore, this study proposed a machine learning model for an ensemble approach to forecasting energy consumption in non-residential buildings. Various datasets from non-residential buildings were collected to assess the predictive performance. Artificial neural networks, support vector regression, and M5Rules models were used as baseline models in this study. Evaluation results have confirmed the effectiveness of the ensemble machine learning model in the next 24-h energy consumption prediction in buildings. The mean absolute error (MAE) and mean absolute percentage error (MAPE) obtained by the ensemble machine learning model were 2.858 kWh and 16.141 kWh, respectively. The ensemble machine learning model can improve the MAE by 123.4% and the MAPE by 209.3% as compared to baseline models. This study contributes to highlighting the advantages of machine learning applications for the building sector. Ensemble machine learning models can be proposed as an effective method for forecasting energy consumption in buildings.
تدمد: 2191-4281
2193-567X
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::6950234eae3581f8c09b262fc7b40122
https://doi.org/10.1007/s13369-021-05927-7
حقوق: CLOSED
رقم الأكسشن: edsair.doi...........6950234eae3581f8c09b262fc7b40122
قاعدة البيانات: OpenAIRE
الوصف
تدمد:21914281
2193567X