Analysis of energy signatures and planning of heating and domestic hot water energy use in buildings in Norway

Tymofii Tereshchenko,; Dmytro Ivanko; Natasa Nord; Igor Sartori

doi:10.1051/e3sconf/201911106009

All issues

Volume 111 (2019)

E3S Web Conf., 111 (2019) 06009

References

Open Access

Issue		E3S Web Conf. Volume 111, 2019 CLIMA 2019 Congress


Article Number		06009
Number of page(s)		8
Section		Sustainable Urbanization and Energy System Integration
DOI		https://doi.org/10.1051/e3sconf/201911106009
Published online		13 August 2019

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[1] Dahl M, Brun A, Andresen GB. Using ensemble weather predictions in district heating operation and load forecasting. Applied Energy. 2017; 193: 455–65. [Google Scholar]

[2] Zhao H-x, Magoulès F. A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews. 2012; 16 (6): 3586–92. [CrossRef] [Google Scholar]

[3] Jovanović RŽ, Sretenović AA, Živković BD. Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings. 2015; 94: 189–99. [Google Scholar]

[4] Catalina T, Iordache V, Caracaleanu B. Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings. 2013; 57: 302–12. [Google Scholar]

[5] Amasyali K, El-Gohary NM. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews. 2018; 81: 1192–205. [CrossRef] [Google Scholar]

[6] Wang Z, Srinivasan RS. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews. 2017; 75: 796–808. [CrossRef] [Google Scholar]

[7] Catalina T, Virgone J, Blanco E. Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings. 2008; 40 (10): 1825–32. [Google Scholar]

[8] Bauer M, Scartezzini JL. A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings. Energy and Buildings. 1998; 27 (2): 147–54. [Google Scholar]

[9] Aranda A, Ferreira G, Mainar-Toledo MD, Scarpellini S, Llera Sastresa E. Multiple regression models to predict the annual energy consumption in the Spanish banking sector. Energy and Buildings. 2012; 49: 380–7. [Google Scholar]

[10] Farzana S, Liu M, Baldwin A, Hossain MU. Multimodel prediction and simulation of residential building energy in urban areas of Chongqing, South West China. Energy and Buildings. 2014; 81: 161–9. [Google Scholar]

[11] Lindberg KB, Doorman G. Hourly load modelling of non-residential building stock. Conference Hourly load modelling of non-residential building stock. p. 1-6. [Google Scholar]

[12] Ryan SE, Porth LSJGTRR-G-FC, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. 41 p. A tutorial on the piecewise regression approach applied to bedload transport data. 2007;189. [Google Scholar]

[13] Energy LPLMoBiM, Distribution Systems PDd, Dept. Process Engineering, The Norw. Univ. of Science and Techn. (NTNU) T, 2007 [Google Scholar]

[14] Djuric N, Novakovic V. Identifying important variables of energy use in low energy office building by using multivariate analysis. Energy and Buildings. 2012; 45: 91–8. [Google Scholar]

[15] Altman DG, Gardner MJJBmj. Statistics in Medicine: Calculating confidence intervals for regression and correlation. 1988;296 (6631): 1238. [Google Scholar]