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Title	Forecasting Renewable Energy Generation: A Predictive Modelling Approach
ID_Doc	26854
Authors	Mondal P.; Bhatia A.; Panjwani R.; Dokare I.
Year	2024
Published	2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024 - Proceedings
DOI	http://dx.doi.org/10.1109/ICSSAS64001.2024.10760410
Abstract	India's rapid urbanization demands innovative solutions to address energy consumption patterns while reducing reliance on fossil fuels. This research paper explores the application of predictive modelling techniques like Multilayer Perceptron and Polynomial Regression to forecast solar energy generation and Long Short-Term Memory (LSTM) models to forecast wind energy generation, thereby facilitating efficient energy planning in major Indian cities. The proposed system aims to create a web-based platform that integrates these predictive models to display real-time temperature conditions and the corresponding amount of energy that solar and wind sources can provide in specific locations, thus promoting smart cities and smart homes. By utilizing the website, energy planners will be able to compare the generated energy from wind and solar sources, enabling informed decisions on which resource best meets the energy requirements of housing settlements. © 2024 IEEE.
Author Keywords	Long Short Term Memory Model; Multilayer Perceptron Network; Solar Energy; Wind Energy

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