AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912145336172544 |
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| author | Tzortzis, Alexandros Menelaos Kormpakis, Georgios Pelekis, Sotiris Michalitsi-Psarrou, Ariadni Karakolis, Evangelos Ntanos, Christos Askounis, Dimitris |
| author_facet | Tzortzis, Alexandros Menelaos Kormpakis, Georgios Pelekis, Sotiris Michalitsi-Psarrou, Ariadni Karakolis, Evangelos Ntanos, Christos Askounis, Dimitris |
| contents | AI4EF, Artificial Intelligence for Energy Efficiency, is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF enables stakeholders such as public sector representatives, energy consultants, and building owners to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modeling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the platform incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. Its compatibility with open-source identity management, Keycloak, enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an architectural overview of AI4EF, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04045 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector Tzortzis, Alexandros Menelaos Kormpakis, Georgios Pelekis, Sotiris Michalitsi-Psarrou, Ariadni Karakolis, Evangelos Ntanos, Christos Askounis, Dimitris Machine Learning AI4EF, Artificial Intelligence for Energy Efficiency, is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF enables stakeholders such as public sector representatives, energy consultants, and building owners to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modeling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the platform incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. Its compatibility with open-source identity management, Keycloak, enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an architectural overview of AI4EF, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI). |
| title | AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.04045 |