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Seasonal autoregressive modelling of water and fuel consumptions in buildings

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  • Lowry, Gordon
  • Bianeyin, Felix U.
  • Shah, Nirav

Abstract

In order to account for variations in the performances of buildings, it is necessary to construct explanatory models of water and energy consumptions. In this paper, a modelling approach is illustrated for those parts of the variances in consumptions of water and energy that are neglected in conventional monitoring and targeting procedures. It is shown that these parts of the consumption variance need not be random and that identifying an autoregressive component can generate better models. Additionally, conventional procedures do not exploit the seasonality that is common in many buildings. Such improved models permit the more reliable detection of significant changes in a building's performance, and more accurate estimations of the effects of changes, whether the result of plant faults or operator intervention.

Suggested Citation

  • Lowry, Gordon & Bianeyin, Felix U. & Shah, Nirav, 2007. "Seasonal autoregressive modelling of water and fuel consumptions in buildings," Applied Energy, Elsevier, vol. 84(5), pages 542-552, May.
    • Handle: RePEc:eee:appene:v:84:y:2007:i:5:p:542-552
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    References listed on IDEAS

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    1. Ozturk, Harun Kemal & Ceylan, Halim & Canyurt, Olcay Ersel & Hepbasli, Arif, 2005. "Electricity estimation using genetic algorithm approach: a case study of Turkey," Energy, Elsevier, vol. 30(7), pages 1003-1012.
    2. Gonzales Chavez, S & Xiberta Bernat, J & Llaneza Coalla, H, 1999. "Forecasting of energy production and consumption in Asturias (northern Spain)," Energy, Elsevier, vol. 24(3), pages 183-198.
    3. M. Froukh, 2001. "Decision-Support System for Domestic Water Demand Forecasting and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 15(6), pages 363-382, December.
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    Cited by:

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    2. Gajda, Janusz & Bartnicki, Grzegorz & Burnecki, Krzysztof, 2018. "Modeling of water usage by means of ARFIMA–GARCH processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 644-657.
    3. Newsham, Guy R. & Birt, Benjamin J. & Rowlands, Ian H., 2011. "A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use," Energy Policy, Elsevier, vol. 39(10), pages 6376-6389, October.
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    5. Zheng, Zhuang & Chen, Hainan & Luo, Xiaowei, 2019. "A Kalman filter-based bottom-up approach for household short-term load forecast," Applied Energy, Elsevier, vol. 250(C), pages 882-894.

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