With the Paris Agreement entering into full force, South Korea must submit its target greenhouse gas emissions for commercial buildings by 2030 to the United Nations Framework Convention on Climate Change. To determine this target, the annual energy demands must be forecasted through appropriate models; the development of these models is the focus of our study. We developed a system to calculate energy demand forecasts by searching for suitable methods. We built distinct energy forecast models for petroleum, city gas, electricity, heat, and renewable energies. The results show that the most appropriate variable for the petroleum energy model is energy trend. Moreover, the annual increase rate of petroleum energy demand from 2019 to 2030 was forecasted to be −1.7%. The appropriate variable for city gas energy model was the floor area of commercial buildings, which was forecasted to increase at an annual average growth rate of 0.4% from 2019 to 2030. According to the forecast results of energy demand from 2019 to 2030, the annual average growth rates of electricity, heat, and renewable energy demands were 2.1%, −0.2%, and 1.3%, respectively.
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study