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Title	Empowering Solar Energy With Advanced Iot-Based Forecasting: A Hybrid Deep Learning Model For Enhanced Efficiency With Big Data
ID_Doc	22945
Authors	Dhanwanth B.; Dhanasakkaravarthi B.; Belshi J.V.G.; Mohanaprakash T.A.; Saranya S.; Naveen P.
Year	2023
Published	International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings
DOI	http://dx.doi.org/10.1109/ICSCNA58489.2023.10370040
Abstract	Solar power is a sustainable energy source that converts sunlight into electricity through photovoltaic panels in large solar facilities. Inverters play a crucial role by converting DC power to usable AC power, albeit with some energy loss. Efficiency relies on factors like solar irradiance, clear skies, clean panels, and unhindered sun exposure. Sensors on panels and inverters in large-scale installations monitor performance and aid in forecasting power generation and maintenance. The Internet of Things (IoT) facilitates remote monitoring and data accessibility, streamlining site assessment for solar power. Smart systems with sensors connected to an Arduino enable continuous monitoring of panel parameters. Short-term energy generation forecasting models are essential for integrating solar systems into smart grids, especially in smart cities. An advanced hybrid deep learning model combines clustering techniques, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms to provide accurate energy generation forecasts. The model consists of three stages: Big Data clustering, training, and forecasting. Clustering identifies relevant historical data, and the training stage constructs a hybrid machine learning model. The testing stage selects the best model, leading to significant improvements in prediction accuracy compared to traditional methods. © 2023 IEEE.
Author Keywords	Big data; Convolutional neural network; Long short-term memory; Machine Learning; Photovoltaic

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