Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter. Alharbi, A. H., Khafaga, D. S., Zaki, A. M., El-Kenawy, E. M., Ibrahim, A., Abdelhamid, A. A., Eid, M. M., El-Said, M., Khodadadi, N., Abualigah, L., & Saeed, M. A. Frontiers in Energy Research, May, 2024. Publisher: Frontiers![Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex \textlessp\textgreaterEnergy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.\textless/p\textgreater
@article{alharbi_forecasting_2024,
title = {Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter},
volume = {12},
issn = {2296-598X},
url = {https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1393794/full},
doi = {10.3389/fenrg.2024.1393794},
abstract = {{\textless}p{\textgreater}Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.{\textless}/p{\textgreater}},
language = {English},
urldate = {2024-08-17},
journal = {Frontiers in Energy Research},
author = {Alharbi, Amal H. and Khafaga, Doaa Sami and Zaki, Ahmed Mohamed and El-Kenawy, El-Sayed M. and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Eid, Marwa M. and El-Said, M. and Khodadadi, Nima and Abualigah, Laith and Saeed, Mohammed A.},
month = may,
year = {2024},
note = {Publisher: Frontiers},
keywords = {machine learning, energy efficiency, Waterwheel Plant Algorithm, Cooling/ Heating Loads, Grey Wolf optimization, Hyperparameter Tunning, Multilayer Perceptron},
file = {Full Text:C\:\\Users\\Ahmed\\Zotero\\storage\\KLHHAGEF\\Alharbi et al. - 2024 - Forecasting of energy efficiency in buildings usin.pdf:application/pdf},
}
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Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. 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