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Akter, Mst. Tamanna
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Review on Employing Weather Forecasts for Microgrids to Predict Solar Energy Generation with IoT and Artificial Neural Networks Islam, Md Monirul; Akter, Mst. Tamanna; Tahrim, H M; Elme, Nafisa Sultana; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.108

Abstract

In this study, an artificial neural network (ANN) based approach is studied about the prediction of solar energy generation in a microgrid using weather forecasting. The ANN is trained using historical data of solar energy generation and weather forecast data. The input parameters for the ANN include weather variables such as temperature, humidity, wind speed, and solar irradiance. The output parameter is the solar energy generation in kilowatt-hour (kWh). The proposed approach is implemented and tested using real-world data from a microgrid. The results indicate that the ANN-based approach is effective in predicting the solar energy generation with high accuracy. The proposed approach can be used for optimizing the operation of microgrids and facilitating the integration of renewable energy sources into the power grid. This study proposes the use of an Artificial Neural Network (ANN) to predict the solar energy generation in a microgrid using weather forecast data. Weather forecasting has become more precise and dynamic with the integration of IoT data with advanced analytics and machine learning models. These models are quite accurate at predicting solar irradiance and analyzing patterns. The microgrid comprises of a photovoltaic (PV) system which generates solar energy and a battery storage system which stores and supplies the energy to the load. Accurate prediction of solar energy generation is crucial for optimizing management of the microgrid. The inputs to the ANN model include temperature, humidity, wind speed, cloud cover and solar irradiance, which are obtained from weather forecast data. The output of the model is the predicted solar energy generation. The performance of the ANN model is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). This study presents a practical approach for predicting solar energy generation in a microgrid using weather forecast data, which can be used for efficient management of the microgrid.
Comparative Analysis of IoT and AI-Based Control Strategies for Community Micro-Grids Islam, Md Monirul; Akter, Mst. Tamanna; Elme, Nafisa Sultana; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.191

Abstract

The main objective of this paper is to review the centralized, decentralized, and hybrid control approaches based on key performance metrics such as efficiency, reliability, and scalability. By improving sustainability, dependability, and efficiency, the combination of artificial intelligence (AI) and the Internet of Things (IoT) in community micro-grids has completely changed energy management. The Internet of Things (IoT) and artificial intelligence (AI) have been used more and more in microgrid control to improve autonomy, dependability, and efficiency. Sensors, smart meters, distributed energy resources (DERs), and energy storage systems are just a few of the microgrid's components that can communicate and monitor in real time thanks to the Internet of Things.AI uses this data to make smart decisions on activities like fault detection, load forecasting, renewable energy prediction, and optimal power dispatch.  To optimize power distribution, load balancing, and fault detection in micro-grids, this article compares several control systems that make use of IoT and AI. The study looks at decentralized, hybrid, and centralized control strategies, emphasizing their benefits, drawbacks, and applicability in various operational scenarios. Important performance indicators are assessed, including cost-effectiveness, responsiveness, energy efficiency, and flexibility about renewable energy sources. The results contribute to the development of smart energy systems by shedding light on the best control schemes for enhancing microgrid performance.
Co-Authors Elme, Nafisa Sultana ISLAM, MD MONIRUL Khan, Md. Yakub Ali Tahrim, H M