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 Reviews,
81, 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 buildings,
38(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 Economics,
51(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 Buildings,
59, 214-227. (
Link)
Kontokosta, C. E., & Tull, C. (2017). A data-driven predictive model of city-scale energy use in buildings.
Applied Energy,
197, 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). (
LinkMiller, C., Nagy, Z., & Schlueter, A. (2015). Automated daily pattern filtering of measured building performance data.
Automation in Construction,
49, 1-17 (
Link).