Selecting the appropriate scenario for forecasting energy demands of residential and commercial sectors in Iran using two metaheuristic algorithms

This study focuses on the forecasting of energy demands of residential and commercial sectors using linear and exponential functions. The coefficients were obtained from genetic and particle swarm optimization (PSO) algorithms. Totally, ۷۲ different scenarios with various inputs were investigated. Consumption data in respect of residential and commercial sectors in Iran were collected from the annual reports of the central bank, Ministry of Energy and the Petroleum Ministry of Iran (۲۰۱۰). The data from ۱۹۶۷ to ۲۰۱۰ were considered for the case of this study. The available data were used partly to obtain the optimal, or near optimal values of the coefficient parameters (۱۹۶۷–۲۰۰۶) and for testing the models (۲۰۰۷–۲۰۱۰). Results show that the PSO energy demand estimation exponential model with inputs, including value addition of all economic sectors, value of constructed buildings, population, and price indices of electrical and fuel appliances using the mean absolute percentage error on tests data were ۱.۹۷%, was considered the most suitable model. Finally, basing on the best scenario, the energy demand of residential and commercial sectors is estimated at ۱۷۱۸ mega barrels of oil equivalent up to the year ۲۰۳۲.

کلیدواژه ها:

energy demand ، forecasting ، Genetic Algorithm ، Particle Swarm Optimization Algorithm ، Residential and commercial sectors