Modeling of water usage by means of ARFIMA–GARCH processes

Janusz Gajda, Grzegorz Bartnicki and Krzysztof Burnecki

Physica A: Statistical Mechanics and its Applications, 2018, vol. 512, issue C, 644-657

Abstract: This paper addresses an important problem of modeling and prediction of phenomena with antipersistent behavior and variance changing in time. As a proper stochastic model we propose an autoregressive fractionally integrated moving average (ARFIMA) process with generalized autoregressive conditional heteroskedasticity (GARCH) noise. First, we introduce a simple identification and validation algorithm for such model. Second, we apply the algorithm to weekday data of hot water usage at urban residential blocks. We extract the deterministic sinusoidal component from the data and fit successfully the ARFIMA–GARCH model to the stochastic part. The goodness of fit is checked by examining model errors and prediction performance. All analyses are performed by the rigorous statistical procedure. The proposed model allows for real-time accurate predictions and when implemented at a hot water supply level will lead to a better optimization of the control system and energy efficiency use.

Keywords: ARFIMA model; GARCH model; Long-range dependence; Water usage (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843711831077X
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:512:y:2018:i:c:p:644-657

DOI: 10.1016/j.physa.2018.08.134

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().