Open AccessModeling and Forecasting of Energy Demands for Household Applications
Author(s) -
Salam Md. Abdus,
Yazdani Md. Gholam,
Wen Fushuan,
Rahman Quazi Mehbubar,
Malik Owais Ahmed,
Hasan Syeed
Publication year - 2020
Publication title -
global challenges
Language(s) - English
Resource type - Journals
ISSN - 2056-6646
DOI - 10.1002/gch2.201900065
Subject(s) - autoregressive integrated moving average , autoregressive model , energy (signal processing) , energy consumption , artificial neural network , spline interpolation , solar energy , interpolation (computer graphics) , environmental science , moving average , econometrics , computer science , meteorology , time series , statistics , engineering , mathematics , geography , telecommunications , artificial intelligence , electrical engineering , frame (networking) , bilinear interpolation
Energy 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 m 2 solar heater and 90 m 2 of solar panel can meet these energy requirements.