References:
The following papers has been reviewed to build the scope of the proposed study.
Amasyali, K., & El-Gohary, N. M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews81, 1192-1205. (Link)
Department of Energy, U.S. (2011). Buildings Energy Databook. Energy Efficiency & Renewable Energy Department. (Link)
Ghiaus, C. (2006). Experimental estimation of building energy performance by robust regression. Energy and buildings38(6), 582-587.(Link)
Kontokosta, C. E. (2015). A market-specific methodology for a commercial building energy performance index. The Journal of Real Estate Finance and Economics51(2), 288-316. (Link)
Korolija, I., Zhang, Y., Marjanovic-Halburd, L., & Hanby, V. I. (2013). Regression models for predicting UK office building energy consumption from heating and cooling demands. Energy and Buildings59, 214-227. (Link)
Kontokosta, C. E., & Tull, C. (2017). A data-driven predictive model of city-scale energy use in buildings. Applied Energy197, 303-317. (Link)
Jain, R. K., Damoulas, T., & Kontokosta, C. E. (2014). Towards Data-Driven Energy Consumption Forecasting of Multi-Family Residential Buildings: Feature Selection via The Lasso. In Computing in Civil and Building Engineering (2014) (pp. 1675-1682). (Link
Miller, C., Nagy, Z., & Schlueter, A. (2015). Automated daily pattern filtering of measured building performance data. Automation in Construction49, 1-17 (Link).