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AI-Driven Control and Processing System for Smart Homes with Solar Energy

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  • Muhammad Hamza

    (Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan)

Abstract

In recent years, the utilization of solar energy has grabbed attention in the industrial and domestic zones. The existing systems to use the services of solar cells are conventional. These systems require parameters (irradiance and temperature) for desirable results that are unknown to the end user. These parameters change with regions and human to human. Therefore, an Artificially Intelligent, Control and Processing System is designed to get more accurate results with the unique feature of empowering the end user, which uses the parameters assembled on different regions. The proposed system has an improved PV model based on (ANN) that resembles experimental results with a few readily available, reprogrammable input parameters from the PV module datasheet. The developed system uses regional irradiation data which exhibits minimal fluctuations. In the model presented here;to avoid overburdening problems, loads weredivided into manageable chunks MK. In this case load chunks (needed) weremoved from solar to utility more stably and economically. Briefly statedthe suggested solution provides a complete package for integrating solar energy systems with the grid in an automated and resilient way.

Suggested Citation

  • Muhammad Hamza, 2024. "AI-Driven Control and Processing System for Smart Homes with Solar Energy," International Journal of Innovations in Science & Technology, 50sea, vol. 6(4), pages 2104-2116, December.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:4:p:2104-2116
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    References listed on IDEAS

    as
    1. Kinga Stecuła & Radosław Wolniak & Wieslaw Wes Grebski, 2023. "AI-Driven Urban Energy Solutions—From Individuals to Society: A Review," Energies, MDPI, vol. 16(24), pages 1-34, December.
    2. Neetzow, Paul & Mendelevitch, Roman & Siddiqui, Sauleh, 2019. "Modeling coordination between renewables and grid: Policies to mitigate distribution grid constraints using residential PV-battery systems," Energy Policy, Elsevier, vol. 132(C), pages 1017-1033.
    3. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    4. Pierre Bouquet & Ilya Jackson & Mostafa Nick & Amin Kaboli, 2024. "AI-based forecasting for optimised solar energy management and smart grid efficiency," International Journal of Production Research, Taylor & Francis Journals, vol. 62(13), pages 4623-4644, July.
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