Hybrid artificial intelligence model for prediction of heating energy use
2022
Аутори
Sretenović, AleksandraJovanović, Radiša
Novaković, Vojislav M.
Nord, Nataša M.
Živković, Branislav
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are difficult to adequately quantify. For heating energy use modelling, the complex relationship between the input and output variables is hard to define. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using (Afferent statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple linear regression was selected for the linear modelling, while the non-linear part was predicted using feedforward and radial bas...is neural networks. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that both hybrid models achieved better results than each of the individual feedforward and radial basis neural networks and multiple linear regression on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.
Кључне речи:
neural networks / hybrid model / heating energy use prediction / artificial intelligenceИзвор:
Thermal Science, 2022, 26, 1, 705-716Издавач:
- Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd
DOI: 10.2298/TSCI210303152S
ISSN: 0354-9836
WoS: 000765408500025
Scopus: 2-s2.0-85124725681
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Sretenović, Aleksandra AU - Jovanović, Radiša AU - Novaković, Vojislav M. AU - Nord, Nataša M. AU - Živković, Branislav PY - 2022 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3702 AB - Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are difficult to adequately quantify. For heating energy use modelling, the complex relationship between the input and output variables is hard to define. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using (Afferent statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple linear regression was selected for the linear modelling, while the non-linear part was predicted using feedforward and radial basis neural networks. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that both hybrid models achieved better results than each of the individual feedforward and radial basis neural networks and multiple linear regression on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models. PB - Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd T2 - Thermal Science T1 - Hybrid artificial intelligence model for prediction of heating energy use EP - 716 IS - 1 SP - 705 VL - 26 DO - 10.2298/TSCI210303152S ER -
@article{ author = "Sretenović, Aleksandra and Jovanović, Radiša and Novaković, Vojislav M. and Nord, Nataša M. and Živković, Branislav", year = "2022", abstract = "Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are difficult to adequately quantify. For heating energy use modelling, the complex relationship between the input and output variables is hard to define. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using (Afferent statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple linear regression was selected for the linear modelling, while the non-linear part was predicted using feedforward and radial basis neural networks. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that both hybrid models achieved better results than each of the individual feedforward and radial basis neural networks and multiple linear regression on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.", publisher = "Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd", journal = "Thermal Science", title = "Hybrid artificial intelligence model for prediction of heating energy use", pages = "716-705", number = "1", volume = "26", doi = "10.2298/TSCI210303152S" }
Sretenović, A., Jovanović, R., Novaković, V. M., Nord, N. M.,& Živković, B.. (2022). Hybrid artificial intelligence model for prediction of heating energy use. in Thermal Science Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd., 26(1), 705-716. https://doi.org/10.2298/TSCI210303152S
Sretenović A, Jovanović R, Novaković VM, Nord NM, Živković B. Hybrid artificial intelligence model for prediction of heating energy use. in Thermal Science. 2022;26(1):705-716. doi:10.2298/TSCI210303152S .
Sretenović, Aleksandra, Jovanović, Radiša, Novaković, Vojislav M., Nord, Nataša M., Živković, Branislav, "Hybrid artificial intelligence model for prediction of heating energy use" in Thermal Science, 26, no. 1 (2022):705-716, https://doi.org/10.2298/TSCI210303152S . .