CENTRE D’EXCELLENCE REGIONAL POUR LA MAÎTRSE DE L’ELECTRICITE (CERME)

Non-Intrusive Load Monitoring (NILM), Interests and Applications

27 Mai 2023

Leonce Wehnelt TOKAM, titoleonce@gmail.com
Centre d’Excellence Regional pour la Maıtrise de l’Electricite (CERME), University of Lome, Lome, Togo

Sanoussi S. OURO-DJOBO
Centre d’Excellence Regional pour la Maıtrise de l’Electricite (CERME), University of Lome, Lome, Togo | Laboratory on Solar Energy, Department of Physics, Faculty of Science, University of Lome, Lome, Togo

Keywords: Non-Intrusive Load Monitoring (NILM), Interests, Applications, Power Consumption

ABSTRACT
In developing effective energy management mechanisms, new concepts have been developed to provide new approaches. Non-intrusive load monitoring (NILM) is an approach that was originally developed to allow the occupants of a room to identify the contribution of each appliance to the total electricity consumption of the room through a single point measurement device. The aim is to provide customers with information that will enable them to act as `` ` consum'actors", i.e., people who undertake to change their electricity consumption habits for an objective cause. The progress of artificial intelligence in its various forms (machine learning, big data, internet of things) have greatly contributed to increase the interest of NILM among researchers in different fields. Indeed, some of them are adapting this concept to research areas such as water, transport, health, the environment and agriculture. In this context, applications in these fields have been developed to show the potential and benefits of using this approach. In addition to presenting non-intrusive load monitoring (NILM) in its general framework, this article presents the interests and applications of this approach in various fields.

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Comparative Study on Load Monitoring Approaches

06 Mai 2023

Leonce W. Tokam 1,* and Sanoussi S. Ouro-Djobo 1,2,*
1 Centre d’Excellence Régional pour laMaitrise de l’Électricité (CERME), University of Lome,
Lome 01 BP 1515, Togo
2 Solar Energy Laboratory, Department of Physics, Faculty of Science, University of Lome, Lome 01 BP 1515, Togo
* Correspondence: wtokam@univ-lome.tg (L.W.T.); sourodjobo@univ-lome.tg (S.S.O.-D.)

Abstract: Without an appropriate monitoring system, the condition/state of electrical appliances/devices in operation in households cannot be fully assessed, resulting in uncontrolled expenses. The purpose of load monitoring techniques is to save electricity consumption. With proper controls, overconsumption of energy can be reduced and unwanted activity that can lead to unnecessary electricity consumption can be eliminated. To achieve this, two approaches are used. The first approach, which says that each device is monitored by means of individual meters or metering devices, is called intrusive load monitoring (ILM) and requires expensive deployment of metering devices for its use. In contrast to the first one, the second approach is non-intrusive load monitoring (NILM), which monitors electricity consumption without the need for any intrusion. In this configuration, the total energy consumed is disaggregated into the individual consumption of each load. With progress/advances in artificial intelligence, this approach is gaining interest with influences in other areas of research. Knowing that these developed techniques aim to encourage the occupants of dwellings to save energy by optimizing their electricity consumption, the paper presents a comparative study of these approaches, in order to highlight the strengths as well as the weaknesses of each of them. It is therefore a means of offering researchers the opportunity to make choices according to the orientations given to the research work.

Keywords: electricity consumption; intrusive load monitoring; non-intrusive load monitoring

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WIND ENERGY POTENTIAL ESTIMATION USING NEURAL NETWORK AND SVR APPROACHES

29 Mars 2023

Adekunlé Akim Salami1* – Pierre Akuété Agbessi1 – Seibou Boureima 2 –Ayité S. Akoda Ajavon 1

1 Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs, Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, P.O. Box: 1515 Lomé, TOGO
2 Mines, Industry and Geology school of Niamey, Niger

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Machine learning prediction of fuel properties of hydrochar from co-hydrothermal carbonization of sewage sludge and lignocellulosic biomass

01 Mars 2023

Oraléou Sangué Djandja a b e, Shimin Kang a, Zizhi Huang a, Junqiao Li a, Jiaqi Feng a, Zaiming Tan a, Adekunlé Akim Salami c, Bachirou Guene Lougou d

a Engineering Research Center of None-food Biomass Efficient Pyrolysis and Utilization Technology of Guangdong Higher Education Institutes, Guangdong Provincial Key Laboratory of Distributed Energy Systems, Dongguan University of Technology, Dongguan, Guangdong, 523808, China
b School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
c Centre D'Excellence Régional pour La Maîtrise de L'Electricité (CERME), Université de Lomé, Lomé, BP 1515, Togo
d School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China
e Organization of African Academic Doctors (OAAD), Off Kamiti Road, P. O. Box 25305000100, Nairobi, Kenya

Received 22 September 2022, Revised 19 January 2023, Accepted 14 February 2023, Available online 24 February 2023, Version of Record 1 March 2023.

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Modelling the Optimal Electricity Mix for Togo by 2050 Using OSeMOSYS

28 Fevrier 2023

Esso-Wazam Honoré Tchandaoa , Akim Adekunlé Salamia,b* , Koffi Mawugno Kodjoa,b ,Amy Nabilioua,b , Seydou Ouedraogoc

a Centre d'Excellence Régional pour la Maîtrise de l'Electricité (CERME), Université de Lomé, 01 BP 1515 Lomé 01, Togo
b Département de Génie Electrique, École Nationale Supérieure d'Ingénieurs (ENSI), Université de Lomé, 01 BP 1515 Lomé 01, Togo
c Laboratoire de Recherche en Sciences de l’Ingénieur (LARSI), Département de Génie Électrique, Institut Universitaire de Technologie, Université Nazi BONI, 01BP 1091 Bobo-Dioulasso 01, Burkina Faso

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An Improved Levenberg– Marquardt Approach With a New Reduced Form for the Identification of Parameters of the One-Diode Photovoltaic Model

31 Août 2022

Building a highly accurate model for solar cells and photovoltaic (PV) modules based on experimental data is becoming increasingly important for the simulation, evaluation, control, and optimization of PV systems. Powerful, accurate, and more robust optimization algorithms are needed to solve this problem. In this study, a new optimization approach based on the Levenberg–Marquardt algorithm (ImLM) is proposed to estimate the parameters of PV cells and modules and simulate their electrical behavior under all environmental conditions efficiently and accurately. To avoid the premature convergence of the Levenberg–Marquardt algorithm and the long computation time caused by a bad choice of initial values, we propose a new approach. This is a new reduced form leading to a nonlinear relationship of the series resistance and thus allowing to calculate the optimal initial values of the model parameters. Comparisons with other published methods show that the proposed approach gives not only a more accurate final solution but also a fast convergence speed and a better stability. Furthermore, tests on three PV modules of different technologies (multi-crystalline, thin film, and monocrystalline) reveal that the proposed algorithm performs well at different irradiations and temperatures. These results confirm
that the ImLM approach is a valuable tool and can be an effective and efficient alternative for extracting PV model parameters and simulating PV module behavior under different conditions. [DOI: 10.1115/1.4053624]

Keywords: photovoltaic models, parameter extraction, Levenberg–Marquardt algorithm, new reduced form, statistical analysis

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Development of typical meteorological year for massive renewable energy deployment in Togo

06 Août 2022

Kokou Amega, Yendoubé Laré, Yacouba Moumouni, Ramchandra Bhandari & Saidou Madougou

To cite this article: Kokou Amega, Yendoubé Laré, Yacouba Moumouni, Ramchandra Bhandari & Saidou Madougou (2022): Development of typical meteorological year for massive
renewable energy deployment in Togo, International Journal of Sustainable Energy, DOI: 10.1080/14786451.2022.2109026

To link to this article: https://doi.org/10.1080/14786451.2022.2109026

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Energy efficiency impact on urban residential’s electricity consumption and carbon dioxide reduction: a case study of Lomé, Togo

02 Juin 2022

Kokou Amega · Yendoubé Lare ·
Yacouba Moumouni

Received: 14 July 2020 / Accepted: 2 June 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022

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Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge

31 Janvier 2022

Oraleou Sangue Djandja a, Adekunle Akim Salami b, Zhi-Cong Wang a, Jia Duo c, d, e, **, Lin-Xin Yin a, Pei-Gao Duan a, c, d, e, *

a Shaanxi Key Laboratory of Energy Chemical Process Intensification, School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, PR China
b Centre d'Excellence Regional pour la Maîtrise de l'Electricite (CERME), Universite de Lome, Lome, BP 1515, Togo
c Xinjiang Key Laboratory of Environmental Pollution and Bioremediation, Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi, 830011, China
d National Engineering Technology Research Center for Desert-Oasis Ecological Construction, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road, Urumqi, 830011, Xinjiang, China
e University of Chinese Academy of Sciences, Beijing, 100049, China

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Experimental investigation of hot water cogeneration using a carbonizer $t out with a preheating system

16 Septembre 2021

Pali Kpelou1,2,*, Damgou Mani Kongnine1,2, Roger Asse1,3 and Essowè Mouzou3

1 Department of Physics, Laboratoire sur l’Energie Solaire, Université de Lomé, Lomé, 01BP 1515, Togo;
2 Centre d’Excellence Régional pour la Maîtrise de l’Electricité, Université de Lomé, Lomé, 01BP 1515, Togo;
3 Department of Physics, Laboratoire de Physique des Matériaux et des Composants à Semi-conducteurs, Université de Lomé, Lomé, 01BP 1515, Togo

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Nos publications

Non-Intrusive Load Monitoring (NILM), Interests and Applications

Comparative Study on Load Monitoring Approaches

WIND ENERGY POTENTIAL ESTIMATION USING NEURAL NETWORK AND SVR APPROACHES

Machine learning prediction of fuel properties of hydrochar from co-hydrothermal carbonization of sewage sludge and lignocellulosic biomass

Modelling the Optimal Electricity Mix for Togo by 2050 Using OSeMOSYS

An Improved Levenberg– Marquardt Approach With a New Reduced Form for the Identification of Parameters of the One-Diode Photovoltaic Model

Development of typical meteorological year for massive renewable energy deployment in Togo

Energy efficiency impact on urban residential’s electricity consumption and carbon dioxide reduction: a case study of Lomé, Togo

Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge

Experimental investigation of hot water cogeneration using a carbonizer $t out with a preheating system

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