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General Information
    • ISSN: 2010-3697
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Engineering & Technology Digital Library, ProQuest, Crossref, Electronic Journals Library, DOAJ, Google Scholar, EI (INSPEC, IET).
    • E-mail ijmo@iacsitp.com
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2015 Vol.5(6): 385-392 ISSN: 2010-3697
DOI: 10.7763/IJMO.2015.V5.493

Prediction of Space Heating Energy Consumption in Cabins Based on Multivariate Regression Modelling

D. Wathsala Upamali Perera, Maths Halstensen, and Nils-Olav Skeie
Abstract—There is a growing interest in reducing energy consumption in buildings. The residential building sector is a substantial energy consumer in many countries. Since the energy consumption characteristics of the residential sector are complex, different types of models are investigated by the researchers to assess the performance. Statistical techniques such as multivariate regression correlate the energy-related measures with the influencing variables. Partial Least Squares Regression (PLSR) is an attractive tool for analyzing complex and large data sets related to building energy optimization because it can handle the dimensionality, correlations and noise present in the building related multi-variable problems. This work is about developing a PLSR model that can predict the space heating energy usage of a cabin with respect to ambient weather conditions. The weather conditions have a great influence on the energy performance of buildings. The model predicts the energy consumption with a RMSEP of 0.23 in hourly basis. The results showed that variations in energy consumption could be predicted accurately. Furthermore, the model interpretation identified the most influential parameters such as outdoor temperature and indoor relative humidity. The development of reliable and fast mathematical models to predict the space heating energy usage is beneficial because they can be integrated with Building Energy Management Systems (BEMS). The developed PLSR model fulfills the criteria and can be combined with BEMS and similarly can be combined with a dynamic model of the building to estimate the heating time.

Index Terms—Cabins, energy consumption, multivariate regression, space heating, weather variables.

D. Wathsala Perera is with Telemark University College, Porsgrunn, Norway (e-mail: wathsala.perera@hit.no).
Maths Halstensen and Nils-Olav Skeie are with the Department of Electrical, IT and Cybernetics, Telemark University College, Porsgrunn, Norway (e-mail: Maths.Halstensen@hit.no, Nils-Olav.Skeie@hit.no).

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Cite: D. Wathsala Upamali Perera, Maths Halstensen, and Nils-Olav Skeie, "Prediction of Space Heating Energy Consumption in Cabins Based on Multivariate Regression Modelling," International Journal of Modeling and Optimization vol. 5, no. 6, pp. 385-392, 2015.

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