Modeling and Forecasting of Energy Demands for Household Applications.

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ID: 84607
2020
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Abstract
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 solar heater and 90 m of solar panel can meet these energy requirements.
Reference Key
salam2020modelingglobal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Salam, Md Abdus;Yazdani, Md Gholam;Wen, Fushuan;Rahman, Quazi Mehbubar;Malik, Owais Ahmed;Hasan, Syeed;
Journal Global challenges (Hoboken, NJ)
Year 2020
DOI
10.1002/gch2.201900065
URL
Keywords
energy consumption nar neural network number of houses solar panels solar water heater spline and arima models

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