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  • Open Access

    ARTICLE

    Smart Micro Grid Energy System Management Based on Optimum Running Cost for Rural Communities in Rwanda

    Fabien Mukundufite1,*, Jean Marie Vianney Bikorimana1, Alexander Kyaruzi Lugatona2

    Energy Engineering, Vol.121, No.7, pp. 1805-1821, 2024, DOI:10.32604/ee.2024.051398

    Abstract The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024. However, the aforementioned goal is challenged by households that are scattered in remote areas. So far, Solar Home Systems (SHS) have mostly been applied to increase electricity access in rural areas. SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities. The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study. The renewable energy resources available in Remera are the key sources… More >

  • Open Access

    ARTICLE

    Energy Demand Forecasting Using Fused Machine Learning Approaches

    Taher M. Ghazal1,2, Sajida Noreen3, Raed A. Said4, Muhammad Adnan Khan5,*, Shahan Yamin Siddiqui3,6, Sagheer Abbas3, Shabib Aftab3, Munir Ahmad3

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 539-553, 2022, DOI:10.32604/iasc.2022.019658

    Abstract The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme More >

  • Open Access

    ARTICLE

    A Novel Knowledge-Based Battery Drain Reducer for Smart Meters

    Isma Farah Siddiqui1, Scott Uk-Jin Lee2,*, Asad Abbas3

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 107-119, 2020, DOI:10.31209/2019.100000132

    Abstract The issue of battery drainage in the gigantic smart meters network such as semantic-aware IoT-enabled smart meter has become a serious concern in the smart grid framework. The grid core migrates existing tabular datasets i.e., Relational data to semantic-aware tuples in its Resource Description Framework (RDF) format, for effective integration among multiple components to work aligned with IoT. For this purpose, WWW Consortium (W3C) recommends two specifications as mapping languages. However, both specifications use entire RDB schema to generate data transformation mapping patterns and results large quantity of unnecessary transformation. As a result, smart meters… More >

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