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Smart city article details

Title	Improving Solar Efficiency Via Cnn-Lstm And Cloud-Integrated Iot Prediction
ID_Doc	30920
Authors	Varun P.; Sunitha T.; Nagalingam M.; Nithiya P.; Selvakumaran S.; Mohanaprakash T.A.
Year	2024
Published	Proceedings - 2024 3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024
DOI	http://dx.doi.org/10.1109/ICSADL61749.2024.00060
Abstract	Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.
Author Keywords	Data clustering; Direct current; Internet of Things; Machine Learning; Smart system

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