摘要：AbstractEnergy use is on the rise due to an increase in the number of households and general consumptions. It is important to estimate and forecast the number of houses and the resultant energy consumptions to address the effective and efficient use of energy in future planning. In this paper, the number of houses in Brunei Darussalam is estimated by using Spline interpolation and forecasted by using two methods, namely an autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The NAR model is more accurate in forecasting the number of houses as compared to the ARIMA model. The energy required for water heating and other appliances is investigated and are found to be 21.74% and 78.26% of the total energy used, respectively. Through analysis, it is demonstrated that 9 m2solar heater and 90 m2of solar panel can meet these energy requirements.This paper estimates the energy consumptions in residential areas. The study applies Spline interpolation by using two methods: autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The results show that 21.74% and 78.26% of the total energy are in use for water heating and other applications. The analysis demonstrates that 9 m2solar heater and 90 m2of solar panel can meet these energy requirements.
关键词：energy consumptionNAR neural networknumber of housessolar panelssolar water heaterspline and ARIMA models
