Climatic scenario-based integrated recurrent ensemble model for energy demand forecasting

Abstract

This study presents an improved approach to estimate future energy demand based on climate scenarios, upstream documents, and Machine Learning (ML). To accurately predict future weather conditions, a Regional Climate Model (RCM) is developed. The RCM incorporates an ensemble approach that combines the power of recurrent neural network and recurrent random forest algorithms to analyze relevant data. The demand model incorporates correction coefficients that are derived from behavioral and variations in income, coupled with an ensemble and recurrent ML models. Four scenarios named from the distinctive wildlife are considered for Tehran, I. R. Iran as a real case study for megacities. The average temperature in the Caracal, Striped hyena, and Balochistan black bear scenarios is projected to be 0.91 °C, 2.01 °C, and 2.86 °C higher than the Persian cheetah scenario between 2015 and 2100. Similarly, natural gas demands are estimated to be 0.93, 4.91, and 3.38 times higher than the Persian cheetah scenario by 2050, respectively. The electricity demand is also 14, 3.98, and 3.50 times higher in the Caracal, Striped hyena, and Balochistan black scenarios compared to the Persian cheetah.This study has developed scenarios delineating future temperature conditions, as well as revealing how energy demand changes within climate change. The results of this study precisely identify the influential features for predicting future energy demand and temperature, indicating that utilizing recurrent models is preferable for forecasting future temperature and energy demand.These changes in the energy demand have significant implications for investment in the energy supply sector as well as the greenhouse gas emissions and the air pollution levels. Therefore, it is crucial to prioritize climate scenarios that lead to a decrease in energy demand to reach sustainable zero-emission cities.