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Tahrim, H M
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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.
A Review on Nanotechnology and Its Impact with Challenges on Electrical Engineering Khan, Md Yakub Ali; Elme, Nafisa Sultana; Tahrim, H M; Raza, Kala
Control Systems and Optimization Letters Vol 2, No 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i1.78

Abstract

Nanotechnology has revolutionized the field of electrical engineering, enabling the development of new materials, devices, and systems with unique properties and functionalities. This review article provides an overview of the impact of nanotechnology on electrical engineering, covering various areas such as analogue and digital circuits, power electronics, sensors, and energy harvesting. The article begins by discussing the basics of nanotechnology, Graphene-based Nanotechnology, nanoscience, Nano photonic and its potential impact on electrical engineering. It then focuses on the application of nanotechnology in various fields of electrical engineering, such as the development of high-performance transistors, nanoscale sensors, and efficient energy conversion systems. The article also discusses the challenges associated with the application of nanotechnology in electrical engineering, such as the need for high-precision fabrication   techniques, the   issue   of   reliability   and reproducibility, and the potential health and environmental concerns. Overall, the   review   article   highlights   the   immense   potential   of nanotechnology in electrical engineering and its impact on various fields of research and development. While challenges exist, continued research and development in nanotechnology promise to lead to significant advancements in electrical engineering, enabling the development of more efficient, and sustainable systems and devices.
Co-Authors Akter, Mst. Tamanna Elme, Nafisa Sultana ISLAM, MD MONIRUL Khan, Md Yakub Ali Khan, Md. Yakub Ali Raza, Kala