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

اين مطالعه با استفاده از توابع خطي و نمايي و با ضرايب به‌دست آمده از الگوريتم‌هاي ژنتيكي و انبوه ذرات به پيش‌بيني تقاضاي انرژي بخش خانگي– تجاري ايران پرداخته است. 72 سناريوي مختلف با ورودي‌هاي متفاوت بررسي شد. داده‌هاي مربوط به سال‌هاي 1346 تا 1389 براي توسعه مدل‌ها و انتخاب سناريوي مناسب استفاده شده است. نتايج نشان داد كه مدل نمايي تخمين‌زده شده با استفاده از الگوريتم انبوه ذرات با ورودي‌هاي ارزش‌افزوده كل منهاي بخش نفت، ارزش ساختمان‌هاي ساخته شده، تعداد كل خانوار و شاخص قيمت مصرف انرژي و درصد ميانگين قدرمطلق خطا 97/1% بهترين مدل بوده است كه براساس بهترين سناريو، انرژي بخش خانگي- تجاري ايران 1718 ميليون بشكه نفت خام تا سال 1410 پيش‌بيني شده است.

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, 72 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 (2010). The data from 1967 to 2010 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 (1967–2006) and for testing the models (2007–2010). Results show that the PSO energy demand estimation exponential model with inputs, including value of made buildings of all economic sectors, value of made buildings, population, and price indices of electrical and fuel appliances using the mean absolute percentage error on tests data were 1.97%, was considered the most suitable model. Finally, basing on the best scenario, the energy demand of residential and commercial sectors is estimated at 1718 mega barrels of oil equivalent up to the year 2032.