Forecasting Models for Long-Term Solar Energy Trends: A Weather-Driven Study in Rural India

Abstract

Long-term solar energy forecasting accuracy is an essential factor in building sustainable solar infrastructures in rural India. Solar output is highly sensitive to factors like weather fluctuations, seasonal cycles and regional climate, all of which point towards the need for advanced forecasting methods. The study created a hybrid, weather-driven forecasting framework to improve solar energy long-term prediction accuracy through the use of two statistical time-series models ARIMA and SARIMA and two machine learning ML models Random Forests and Long Short-Term Memory LSTM; to account for both linear seasonal trends and complex non-linear relationships in multivariate data such as solar irradiance, temperature, humidity and cloud cover; trained and evaluated on data covering multiple climate types. The hybrid model achieved a prediction accuracy of 92.8% when comparing with commonly used individual models, where ARIMA and SARIMA accuracies ranged between 84.3% - 88.6%; Random Forest was 89.7%, and LSTM was 91.2%. The hybrid model achieved a Mean Absolute Percentage Error MAPE of less than 7.2% and was proven to be more robust and reliable for predicting long-term solar output across varying climates compared to using statistical-only models and proved the benefit of using statistical and ML modelling combined for forecasting long-term solar generation; provided further support to aid rural energy planning, grid integration and policy creation.