Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board
Efficient management of street lighting is crucial for cities seeking to reduce their energy consumption and greenhouse gas emissions. This paper proposes an innovative approach that dynamically adjusts the brightness of streetlights according to two key factors: traffic density and weather conditio...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_04012.pdf |
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| _version_ | 1841554724244946944 |
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| author | Jouahri Mohammed Amine Moukhtari Manal Oulhaj Nabil Khimouj Mounir Tajer Abdelouahed Boulghasoul Zakaria |
| author_facet | Jouahri Mohammed Amine Moukhtari Manal Oulhaj Nabil Khimouj Mounir Tajer Abdelouahed Boulghasoul Zakaria |
| author_sort | Jouahri Mohammed Amine |
| collection | DOAJ |
| description | Efficient management of street lighting is crucial for cities seeking to reduce their energy consumption and greenhouse gas emissions. This paper proposes an innovative approach that dynamically adjusts the brightness of streetlights according to two key factors: traffic density and weather conditions. Traffic density is assessed in real time by an image processing system using the YOLOv8 algorithm, which identifies and counts vehicles captured by the cameras. At the same time, the level of cloud cover is measured by an LDR photosensor connected to a Raspberry Pi, which analyzes the ambient light intensity. These data are transmitted to the Raspberry Pi via the MQTT protocol, where a neural network model, trained beforehand, predicts the optimal operating cycle of the street lamps to adjust their brightness in real time. The results show that this method, combining machine vision, IoT and artificial intelligence, delivers significant energy savings without compromising user safety, offering a promising solution for modern cities. |
| format | Article |
| id | doaj-art-a8b1cf3ccc05417c80888f09d94d52f1 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-a8b1cf3ccc05417c80888f09d94d52f12025-01-08T10:58:54ZengEDP SciencesITM Web of Conferences2271-20972024-01-01690401210.1051/itmconf/20246904012itmconf_maih2024_04012Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi BoardJouahri Mohammed Amine0Moukhtari Manal1Oulhaj Nabil2Khimouj Mounir3Tajer Abdelouahed4Boulghasoul Zakaria5Systems Engineering and Application Laboratory, Cady Ayyad UniversityDept. SEECS, ENSADept. SEECS, ENSADept. SEECS, ENSASystems Engineering and Application Laboratory, Cady Ayyad UniversitySystems Engineering and Application Laboratory, Cady Ayyad UniversityEfficient management of street lighting is crucial for cities seeking to reduce their energy consumption and greenhouse gas emissions. This paper proposes an innovative approach that dynamically adjusts the brightness of streetlights according to two key factors: traffic density and weather conditions. Traffic density is assessed in real time by an image processing system using the YOLOv8 algorithm, which identifies and counts vehicles captured by the cameras. At the same time, the level of cloud cover is measured by an LDR photosensor connected to a Raspberry Pi, which analyzes the ambient light intensity. These data are transmitted to the Raspberry Pi via the MQTT protocol, where a neural network model, trained beforehand, predicts the optimal operating cycle of the street lamps to adjust their brightness in real time. The results show that this method, combining machine vision, IoT and artificial intelligence, delivers significant energy savings without compromising user safety, offering a promising solution for modern cities.https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_04012.pdfintelligent street lightingenergy efficiencyneural networksinternet of things (iot)yolov8 |
| spellingShingle | Jouahri Mohammed Amine Moukhtari Manal Oulhaj Nabil Khimouj Mounir Tajer Abdelouahed Boulghasoul Zakaria Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board ITM Web of Conferences intelligent street lighting energy efficiency neural networks internet of things (iot) yolov8 |
| title | Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board |
| title_full | Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board |
| title_fullStr | Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board |
| title_full_unstemmed | Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board |
| title_short | Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board |
| title_sort | design of an intelligent energy management prototype for an electric lighting network on a raspberry pi board |
| topic | intelligent street lighting energy efficiency neural networks internet of things (iot) yolov8 |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_04012.pdf |
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