%0 Journal Article %T Estimation and Prediction of Residential Building Energy Consumption in Rural Areas of Chongqing %J International Journal of Engineering %I Materials and Energy Research Center %Z 1025-2495 %A Meng, Liu %A Hossain, Md. %D 2013 %\ 09/01/2013 %V 26 %N 9 %P 955-962 %! Estimation and Prediction of Residential Building Energy Consumption in Rural Areas of Chongqing %K Rural area %K Residential energy %K Simulation %K Grey model %K Forecasting %R %X Energy simulation is a vital part of energy policy of a country, especially for a developing country like China where energy consumption is growing very rapidly. The present study has been conducted to simulate the total primary energy consumption in residential sector in rural areas in Chongqing by using macro and micro drivers including population size, number of households, persons per household, fuel types, end-use devices and their intensities. The study finds the energy intensity of end-use device in rural areas in Chongqing is 1166.15kwh/household/year. About 11.02% energy consumes for lighting, 16.53% for space heating and cooling, 58.71% for cooking and water heating, and 13.74% for other end-use devices in the studied areas. The sharing of fuels are LPG 18.15%, coal 23.54%, firewood 21.13% and electricity 37.18% that are used as primary fuel.  The study finds the total residential energy consumption is43.940X109 kWh/year in rural areas of Chongqing in 2012. The study has also conducted to forecast the energy consumption during 2000-2020 by using two Grey Model such as GM (1,1) and DGM (2,1) in Chongqing. The GM (1,1) uses a first order differential equation to characterize an unknown system in which the irregular data of system can become regular sequences which it can identify the uncertainties of system and predict the variables of it. DGM (2,1) model is a new grey model which is constructed by grey derivative and second-order grey derivative. The five years average growth rate of total energy consumption in Chongqing during 2011-2015 and 2016-2020 are 51.70% and 195.04% respectively comparing to 2010 by GM (1,1) model, whereas 70.54% and 330.23% respectively by DGM (2,1) model during the designated time period. The higher accuracy has been found in GM (1,1) model than in DGM (2,1) model. %U https://www.ije.ir/article_72168_650aa5dd13b919dd7b8eece260f1653f.pdf