Building energy efficiency: using machine learning algorithms to accurately predict heating load

Ahmadi, Monireh

doi:10.1007/s42107-023-00967-w

Asian J Civ Eng

2024

DOI: 10.1007/s42107-023-00967-w

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Building energy efficiency: using machine learning algorithms to accurately predict heating load

Monireh Ahmadi

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Cited by 4 publications

References 72 publications

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Enhancing building sustainability through aerodynamic shading devices: an integrated design methodology using finite element analysis and optimized neural networks

Al-Haddad,

Al-Muslim,

Hammood

et al. 2024

Asian J Civ Eng

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Enhancing building sustainability through aerodynamic shading devices: an integrated design methodology using finite element analysis and optimized neural networks

Al-Haddad,

Al-Muslim,

Hammood

et al. 2024

Asian J Civ Eng

View full text Add to dashboard Cite

Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

Hashemi,

Besharati,

Hashemi

et al. 2024

Energy Storage and Saving

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Exergy Analysis of a District Heating System with a Predictive Model Based on Neural Network Algorithms

Tuponogov,

Sesekin,

Bolkov

2024

Nadežn. bezop. ènerg.

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The article presents an exergy analysis of a district heating system (DHS) that incorporates a predictive model based on neural network algorithms. The study focuses on evaluating the thermodynamic efficiency of the DHS when these methods are applied, enabling the prediction of temperature changes and rapid adjustment of system parameters to enhance efficiency. The impact of increasing the number of connected consumers on the exergy efficiency of the DHS with the predictive model (DHPM) is examined. The use of neural network algorithms, such as LSTM, significantly enhances the system's ability to predict external temperature changes and respond promptly, resulting in optimized energy consumption and improved exergy efficiency, particularly at the beginning and end of the heating season. A comparative analysis of the exergy efficiency of traditional DHS and the system with the predictive model is included. The exergy analysis is based on modeling the system’s performance with varying numbers of consumers. The results indicate that an increase in the number of consumers leads to a rise in exergy efficiency due to better energy distribution management. The potential for significant thermal energy savings and reduced operational costs through the use of predictive methods is discussed. The study confirms that applying neural network algorithms in predictive models of DHS can substantially improve efficiency and reliability, leading to a more rational use of energy resources, cost reduction, and minimized environmental impact. The findings support the adoption of these methods in large-scale district heating systems for optimization and enhanced energy efficiency.

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Building energy efficiency: using machine learning algorithms to accurately predict heating load

Cited by 4 publications

References 72 publications

Enhancing building sustainability through aerodynamic shading devices: an integrated design methodology using finite element analysis and optimized neural networks

Enhancing building sustainability through aerodynamic shading devices: an integrated design methodology using finite element analysis and optimized neural networks

Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

Exergy Analysis of a District Heating System with a Predictive Model Based on Neural Network Algorithms

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