Temperature-Based Renewable Energy Forecasting: A Big Data Analysis for Sustainable Energy Planning in Bangladesh

Sustainability in renewable energy involves utilizing energy sources intelligently to serve the present and yet be accessible for future generations. With Bangladesh shifting to renewable energy due to rising climate threats, it needs expert and data-based input to create sustainable infrastructure. The investigation uses a blend of statistical and deep learning methodologies to analyze the implications of temperature-based climatic patterns on the potential for solar energy in Rajshahi and Ishwardi in Bangladesh. We centered our study on past temperatures from 1980 to 2020, bringing in both linear regression and one of the top deep learning models, LSTM, as ideal ways to estimate climate in the future. It was found that both locations are seeing a significant increase in temperature, whereas the LSTM technique was superior at spotting irregular seasonal fluctuations and trends across years. We utilized the all-sky shortwave irradiance, clearness index of solar radiation, quantity of cloud cover, albedo, and near-surface temperature from the NASA-POWER datasets to estimate the solar energy potential. The research showed that Rajshahi had superior conditions for solar energy owing to the cleaner weather, fewer overcast days, and higher irradiance intensity. This research provides significant insights for regional policy interests and renewable energy planning via extensive visual data and the assessment of several factors. The report advocates for using AI and data to facilitate solar expansion. It highlights the crucial role of deep learning in promoting sustainability and ecologically safe energy in Bangladesh and other developing nations.