An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage
The emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the developmen...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10049578/ |
| _version_ | 1811162826091266048 |
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| author | Enrico Giglio Gabriele Luzzani Vito Terranova Gabriele Trivigno Alessandro Niccolai Francesco Grimaccia |
| author_facet | Enrico Giglio Gabriele Luzzani Vito Terranova Gabriele Trivigno Alessandro Niccolai Francesco Grimaccia |
| author_sort | Enrico Giglio |
| collection | DOAJ |
| description | The emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the development of a methodology for the energy management, combining photovoltaics and storage systems, considering as the main case study a multi-story building characterized by a high density of households, used to generate data which allow feasibility foresights. The physical model of the algorithm is composed by two main elements: the photovoltaics modules and the battery energy storage system. In addition, to gain information about the real-time consumption a machine learning module is included in our approach to generate predictions about the near future demand. The benefits provided by the method are evaluated with an economic analysis, which computes the return of the investment using the real consumptions of a Boarding School, located in Turin (Italy). The case study analyzed in this article showed an increase in purchased energy at the minimum price from 25% to 91% and a 55% reduction in the electricity bill compared to most solutions on the market, with no additional costs and a stabilizing effect on the grid. Finally, the economic analysis shows that the proposed method is a profitable investment, with a breakeven point of thirteen years, due to the very simple implementation and the zero additional cost requested. |
| first_indexed | 2024-04-10T06:36:35Z |
| format | Article |
| id | doaj.art-a6fa99ae30164285855162b0f649d196 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T06:36:35Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a6fa99ae30164285855162b0f649d1962023-03-01T00:01:15ZengIEEEIEEE Access2169-35362023-01-0111186731868810.1109/ACCESS.2023.324763610049578An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and StorageEnrico Giglio0https://orcid.org/0000-0002-9501-9139Gabriele Luzzani1https://orcid.org/0000-0002-6000-7816Vito Terranova2Gabriele Trivigno3Alessandro Niccolai4https://orcid.org/0000-0002-5840-4222Francesco Grimaccia5https://orcid.org/0000-0003-2568-9927Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Turin, ItalyDipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Turin, ItalyDipartimento di Energia, Politecnico di Milano, Milan, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Turin, ItalyDipartimento di Energia, Politecnico di Milano, Milan, ItalyDipartimento di Energia, Politecnico di Milano, Milan, ItalyThe emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the development of a methodology for the energy management, combining photovoltaics and storage systems, considering as the main case study a multi-story building characterized by a high density of households, used to generate data which allow feasibility foresights. The physical model of the algorithm is composed by two main elements: the photovoltaics modules and the battery energy storage system. In addition, to gain information about the real-time consumption a machine learning module is included in our approach to generate predictions about the near future demand. The benefits provided by the method are evaluated with an economic analysis, which computes the return of the investment using the real consumptions of a Boarding School, located in Turin (Italy). The case study analyzed in this article showed an increase in purchased energy at the minimum price from 25% to 91% and a 55% reduction in the electricity bill compared to most solutions on the market, with no additional costs and a stabilizing effect on the grid. Finally, the economic analysis shows that the proposed method is a profitable investment, with a breakeven point of thirteen years, due to the very simple implementation and the zero additional cost requested.https://ieeexplore.ieee.org/document/10049578/Deep learningenergy management systemsenergy storageenvironmental economicsrenewable energy sources |
| spellingShingle | Enrico Giglio Gabriele Luzzani Vito Terranova Gabriele Trivigno Alessandro Niccolai Francesco Grimaccia An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage IEEE Access Deep learning energy management systems energy storage environmental economics renewable energy sources |
| title | An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage |
| title_full | An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage |
| title_fullStr | An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage |
| title_full_unstemmed | An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage |
| title_short | An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage |
| title_sort | efficient artificial intelligence energy management system for urban building integrating photovoltaic and storage |
| topic | Deep learning energy management systems energy storage environmental economics renewable energy sources |
| url | https://ieeexplore.ieee.org/document/10049578/ |
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