<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Artificial intelligence (AI) plays a pivotal role as a technical facilitator in the domain of building energy management. This paper presents the construction of a sophisticated model for building energy management, leveraging data-driven techniques and machine learning methodologies inherent to AI. The model encompasses three integral modules: characterization of building energy consumption, screening for anomalies in energy consumption, and forecasting of energy usage. Characterization is performed through a data analysis approach. At the same time, anomaly detection utilizes a Gaussian mixture model for data mining and modeling, enabling dynamic and precise identification of states of high energy consumption following clustering analysis. Additionally, the model employs cyclic features and DEEM to accurately predict future energy consumption patterns. The efficacy of this robust energy management model was validated through a case study at Hospital A, located in Changsha, Hunan Province, China. Notably, the total energy consumption at this facility witnessed a modest increase of 1.65% in 2023 compared to the previous year, with a projection of a substantial 24.62% rise in 2024. The surgery building, among various functional units of the hospital, was identified as the most energy-intensive, anticipated to utilize 4.03 million kgce in 2024. Furthermore, the disparity between actual electrical energy and oil consumption in 2023 and those forecasted was contained within 8% and 10%, respectively, underscoring the model’s high predictive accuracy.</p>
</abstract>

Artificial intelligence (AI) plays a pivotal role as a technical facilitator in the domain of building energy management. This paper presents the construction of a sophisticated model for building energy management, leveraging data-driven techniques and machine learning methodologies inherent to AI. The model encompasses three integral modules: characterization of building energy consumption, screening for anomalies in energy consumption, and forecasting of energy usage. Characterization is performed through a data analysis approach. At the same time, anomaly detection utilizes a Gaussian mixture model for data mining and modeling, enabling dynamic and precise identification of states of high energy consumption following clustering analysis. Additionally, the model employs cyclic features and DEEM to accurately predict future energy consumption patterns. The efficacy of this robust energy management model was validated through a case study at Hospital A, located in Changsha, Hunan Province, China. Notably, the total energy consumption at this facility witnessed a modest increase of 1.65% in 2023 compared to the previous year, with a projection of a substantial 24.62% rise in 2024. The surgery building, among various functional units of the hospital, was identified as the most energy-intensive, anticipated to utilize 4.03 million kgce in 2024. Furthermore, the disparity between actual electrical energy and oil consumption in 2023 and those forecasted was contained within 8% and 10%, respectively, underscoring the model’s high predictive accuracy.

Accurate Building Energy Management Based on Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Accurate Building Energy Management Based on Artificial Intelligence"}

Artificial intelligence (AI) plays a pivotal role as a technical facilitator in the domain of building energy management. This paper presents the construction...

Accurate Building Energy Management Based on Artificial Intelligence

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 12. Feb. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1359

Schlüsselwörter
Building energy management, Data analysis method, Gaussian mixture model, Cluster analysis, DEEM model

© 2024 Qiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Accurate Building Energy Management Based on Artificial Intelligence

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 12. Feb. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1359

SchlüsselwörterBuilding energy management, Data analysis method, Gaussian mixture model, Cluster analysis, DEEM model

© 2024 Qiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Building energy management, Data analysis method, Gaussian mixture model, Cluster analysis, DEEM model