References:

Abaza, H. (2008). An interactive design advisor for energy-efficient building. Journal of Green Building, 3(1), 112–125.
Abd Rashid, A. F., and Yusoff, S. (2015). A review of life cycle assessment method for building industry. Renewable and Sustainable Energy Reviews, Elsevier, 45, 244–248.
Abdallah, M. and El-Rayes, K. (2015). Optimizing the selection of building upgrade measures to minimize the operational negative environmental impacts of existing buildings. Building and Environment, 84, 32–43.
Abdallah, M. (2014). Optimizing the selection of sustainability measures for existing buildings, Doctoral dissertation, University of Illinois at Urbana-Champaign.
Adam, P., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017). Automatic differentiation in PyTorch. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 2–8.
Afram, A., Janabi-Sharifi, F., Fung, A. S., and Raahemifar, K. (2017). Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state-of-the-art review and case study of a residential HVAC system. Energy and Buildings, Elsevier B.V., 141, 96–113.
Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., and Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, Elsevier, 33, 102–109.
Ahmad, M. W., Mourshed, M., and Rezgui, Y. (2017). Trees vs. Neurons : Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, Elsevier B.V., 147, 77–89.
AIA, The American Institute of Architects (2007). Integrated Project Delivery: A Guide, ver. 1. Retrieved 1, 9, 2017, from http://www.aia.org/ipdg.
Aktas Can B. and Bilec M. M. (2012). Impact of lifetime on US residential building LCA results, The International Journal of Life Cycle Assessment, 3:17, 337-349.
Alev, Ü., Eskola , L., Arumägi, E., and Jokisalo, J. (2014). Renovation alternatives to improve energy performance of historic rural houses in the Baltic sea region. Energy and Buildings, 77, 58–66.
Alshamrani, O., Galal, K., and Alkass, S. (2014). Integrated LCA-LEED sustainability assessment model for structure and envelope systems of school buildings. Building and Environment, 80, 61-70.
Al-Hamadi, H. M., and Soliman, S. A. (2004). Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electric Power Systems Research, 68(1), 47–59. http://doi.org/10.1016/S03787796(03)00150-0
Amasyali, K., and El-Gohary, N. M. (2018a). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, Elsevier Ltd, 81, 1192–1205.
Amasyali, K., and El-Gohary, N. (2018b). Deep learning for building energy consumption prediction. Leadership in Sustainable Infrastructure, CSCE.
Amirifard, F., Sharif, S. A., and Nasiri, F. (2018). Application of passive measures for energy conservation in buildings: a review. Advances in Building Energy Research, Taylor and Francis, 0(0), 1–34.
Anand , C. and Amor, B. (2017). Recent developments, future challenges and new research directions in LCA of buildings: A critical review. Renewable and Sustainable Energy Reviews, 67, 408-416.
Asadi, E., Silva, M. G. da, Antunes, C. H., Dias, L., and Glicksman, L. (2014). Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy and Buildings, Elsevier, 81, 444–456.
Asadi, E., Silva, M. G., Antunes, C. H., and Dias, L. (2013). State of the art on retrofit strategies selection using multi-objective optimization and genetic algorithms. In nearly zero energy building refurbishment (Springer, London), 279–297.
Ascione, F., Bianco, N., De Masi, R. F., De Stasio, C., Mauro, G. M., and Vanoli, G. P. (2017a). Artificial neural networks for predicting the energy behavior of a building category: A powerful tool for cost-optimal analysis. cost-effective energy efficient building retrofitting: materials, technologies, optimization and case studies, Elsevier Ltd.
Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., and Vanoli, G. P. (2017b). CASA, cost-optimal analysis by multi-objective optimization and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building. Energy and Buildings, Elsevier B.V., 146, 200–219.
Asdrubali, F., Baldassarri, C., and Fthenakis, V. (2013). Life Cycle Analysis in the construction sector: Guiding the optimization of conventional Italian buildings. Energy and Buildings, 64, 73-89.
ASHRAE Design Guide (2014). Advanced energy design guide for small to medium office buildings. Book. Atlanta, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
ATHENA Impact Estimator. (2017). Athena Impact Estimator for Buildings V4.2 Software and Database Overview. Retrieved 10, 14, 2017, from https://calculatelca.com/software/impact-estimator/user-manual/
Ayata, T., Arcaklioǧlu, E., and Yildiz, O. (2007). Application of ANN to explore the potential use of natural ventilation in buildings in Turkey. Applied Thermal Engineering, 27(1), 12–20.
Ayoub, N., and Yuji, N. (2012). Governmental intervention approaches to promote renewable energies-special emphasis on Japanese feed-in tariff. Energy Policy, Elsevier, 43, 191–201.
Azari, R., Garshasbi, S., Amini, P., Rashed-Ali, H., and Mohammadi, Y. (2016). Multi-objective optimization of building envelope design for life cycle environmental performance. Energy and Buildings, Elsevier B.V., 126, 524–534.
Azadeh, M. A., and Sohrabkhani, S. (2006). Annual electricity consumption forecasting with neural network in high energy consuming industrial sectors of Iran, Proceedings of the IEEE International Conference on Industrial Technology, 49, 2166 -2171.
Azadeh, M. A., Ghaderi, S.F. and Sohrabkhani, S. (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and management 49.8: 2272-2278.
Bare, J. C., Hofstetter, P., Pennington, D. W., and De Haes, H. A. U. (2000). Midpoints versus Endpoints : The sacrifices and benefits. The International Journal of Life Cycle Assessment, 5(6), 319–326.
Bahrami, S., Hooshmand, R.-A., and Parastegari, M. (2014). Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy, 72(2014), 434–442. http://doi.org/10.1016/j.energy.2014.05.065
Blengini, G. A., and Di Carlo, T. (2010). The changing role of life cycle phases, subsystems and materials in the LCA of low-energy buildings. Energy and Buildings, Elsevier B.V., 42(6), 869–880.
Bocheng, Z., Kuo, L. U., Dinghao, L. V, Jing, L. U. O., and Xuan, F. (2015). Short-term prediction of building energy consumption based on GALM Neural Network. (AMEII), 867–871.
Bolattürk A. (2006). Determination of optimum insulation thickness for building walls with respect to various fuels and climate zones in Turkey, Appl Therm Eng, 26,1301-9.
Bolattürk A. (2008). Optimum insulation thicknesses for building walls with respect to cooling and heating degree hours in the warmest zone of Turkey, Building and Environment, 43, 1055–1064.
Borjesson P. and Gustavsson L. (2000). Greenhouse gas balances in building construction: wood versus concrete from life-cycle and forest land-use perspectives, Energy Policy,28, 575-588.
Bouchlaghem, NM. and Letherman, KM. (1990). Numerical optimization applied to the thermal design of buildings, Build Environ, 25(2),117–124.
Bowick, M., O’Connor, J., and Meil, J. (2014). Athena Guide to whole-building LCA in green building programs,1–41.
Branke, J., Deb, K., and Miettinen, K. (Eds.). (2008). Multiobjective optimization: Interactive and evolutionary approaches (Vol. 5252). Springer Science and Business Media.
Buyle, M., Braet, J., and Audenaert, A. (2013). Life cycle assessment in the construction sector: A review. Renewable and Sustainable Energy Reviews, 26, 379–388.
Cabeza, L., Rincón, L., Vilariño, i., Pérez, G., and Castell, A. (2014). Life Cycle Assessment (LCA) and Life Cycle Energy Analysis (LCEA) of buildings and the building sector: A review. Renewable and Sustainable Energy Reviews, 29, 394-416.
CaGBC the Canadian Green Building Council (CaGBC). Retrieved 2, 14, 2017, from: https://www.cagbc.org/.
CBC News. (2016, 10 5). CBC News. Retrieved 2, 14, 2017, from: http://www.cbc.ca/news/canada/montreal/mcgill-concordia-quebec-buildings-university-1.3793180
Chalal, M. L., Benachir, M., White, M., and Shrahily, R. (2016). Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review. Renewable and Sustainable Energy Reviews, Elsevier, 64, 761–776.
Chang, Y., Huang, Z., Ries, R. j., and Masanet, E. (2016). The embodied air pollutant emissions and water footprints of buildings in China: a quantification using disaggregated input–output life cycle inventory model. Journal of Cleaner Production, 113, 274-284.
Chantrelle, F. P., Lahmidi, H., Keilholz, W., Mankibi, M. El, and Michel, P. (2011). Development of a multicriteria tool for optimizing the renovation of buildings. Applied Energy, Elsevier Ltd, 88(4), 1386–1394.
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P. (2016). Variational Lossy Autoencoder. The International Conference on Learning Representations (ICLR) Conference, 1–17.
Chen, L., Fang, Q., and Zhang, Z. (2010). Research on the identification of temperature in intelligent building based on feedforward neural network and particle swarm optimization algorithm. 2010 6th International Conference on Computer Science and Information Technology, 286–290.
Citroen, N., Ouassaid, M., and Maaroufi, M. (2015). Moroccan long-term electricity demand forecasting using wavelet neural networks. In 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC), IEEE, 1-7.
Collinge, W. O., Thiel, C. L., Campion, N. A., Al-Ghamdi, S. G., Woloschin, C. L., Soratana, K., Landis, A. E., and Bilec, M. M. (2015). Integrating Life Cycle Assessment with green building and product rating systems: North American perspective. Procedia Engineering, Elsevier B.V., 118, 662–669.
Cohon J. L (1978). Multiobjective programming and planning. [Book]. New York: Academic Press.
Choliet, F., (2013). Deep Learning with Python, Manning Publications Co., the United States of America, Manning Publications Co.
Coakley, D., Raftery, P., and Keane, M. (2014). A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews, Elsevier, 37, 123–141.
Conraud-bianchi, J. (2008). A methodology for the optimization of building energy, thermal, and visual performance, thesis in the Department of Building, Building, Civil and Environmental Engineering, Concordia University.
Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Pedersen, C. O., Strand, R. K., Liesen, R. J., Fisher, D. E., Witte, M. J., and Glazer, J. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33(4), 319–331.
Crawford, R. (2008). Validation of a hybrid life cycle inventory analysis method. Journal of Environmental Management, 88, 496–506.
Deb, K., Partap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (2), 182-197.
Deb K., (2001). Multi-objective optimization using evolutionary algorithms. [Book]. New York : John Wiley and Sons.
Delgarm, N., Sajadi, B., Kowsary, F., and Delgarm, S. (2016). Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy, Elsevier Ltd, 170, 293–303.
de Vasconcelos, A. B., Pinheiro, M. D., Manso, A., and Cabaço, A. (2015). A Portuguese approach to define reference buildings for cost-optimal methodologies. Applied Energy, 140, 316-328.
DesignBuilder Software Ltd., (2016). DesignBuilder. Retrieved 1, 1, 2016, from https://www.designbuilder.co.uk/
De Wilde, P. (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction, 41, 40-49.
DiLouie, C. (2008). Lighting Control’s Handbook. NY, Illuminating Engineering Society.
Diwekar U. (2013). Introduction to Applied Optimization. Springer Science and Business Media.
Dixit, M. K., Fernández-Solís, J. L., Lavy, S., and Culp, C. H. (2012). Need for an embodied energy measurement protocol for buildings: A review paper. Renewable and Sustainable Energy Reviews, Pergamon, 16(6), 3730–3743.
DOE-2, Retrieved 1, 5, 2016, from http://doe2.com
Eastman, C., Teicholz , P., Sacks, R., and Liston, K. (2008). BIM handbook: a guide to building information modeling for owners, managers, designers, engineers, and contractors. New York, John Wiley and Sons.
Edwards, R. E., New, J., and Parker, L. E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, Elsevier B.V., 49, 591–603.
e-QUEST, Retrieved 11, 1, 2015, from http://doe2.com/equest/
Energy and Mines Ministers (2018). Canada’s Buildings Strategy Update. Energy and Mines Ministers’ Conference, Iqaluit, Nunavut. (Report)
Escandón, R., Ascione, F., Bianco, N., Maria, G., Suárez, R., and José, J. (2019). Thermal comfort prediction in a building category : Artificial neural network generation from calibrated models for a social housing stock in southern Europe. Applied Thermal Engineering, Elsevier, 150, 492–505.
Espinoza, M., Joye, C., Belmans, R., and De Moor, B. (2005). Short-term load forecasting, profile identification, and customer segmentation: A methodology based on periodic time series. IEEE Transactions on Power Systems, 20(3), 1622–1630. http://doi.org/10.1109/TPWRS.2005.852123
Eisenhower, B., O’Neill, Z., Narayanan, S., Fonoberov, V. A., and Mezić, I. (2012). A methodology for meta-model based optimization in building energy models. Energy and Buildings, 47, 292-301.
Evins, R. (2013). A review of computational optimization methods applied to sustainable building design. Renewable and Sustainable Energy Reviews, Elsevier, 22, 230–245.
Eurostat. (2010). Environment and Energy Europe in figures: Eurostat yearbook. Luxembourg: Office for Official Publications of the European Communities.
Fan, C., Xiao, F., Zhao, Y., and Wang, J. (2018). Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data. Applied Energy, Elsevier, 211, 1123–1135.
Fan, S., and Hyndman, R. J. (2012). Short-term load forecasting based on a semi-parametric additive model. IEEE Transactions on Power Systems, 27(1), 134–141. http://doi.org/Doi 10.1109/Tpwrs.2011.2162082.
Ferreira, P. M., Ruano, A. E., Silva, S., and Conceição, E. Z. E. (2012). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy and Buildings, 55, 238–251.
Freeman, J. A., and Skapura, D. M. (1991). Neural Networks: algorithms, applications, and programming techniques, Loral Space Information Systems and Adjunct Faculty, School of Natural and Applied Sciences University of Houston at Clear Lake, Addison-Wesley Publishing Company.
Flores-colen, I., and Brito, J. De. (2010). A systematic approach for maintenance budgeting of buildings façades based on predictive and preventive strategies. Construction and Building Materials, Elsevier Ltd, 24(9), 1718–1729.
Fuller, S. and Petersen, S. (1996). Life cycle costing manual for the Federal Energy Management Program. Handbook. Gaithersburg, MD, U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology.
Fumo, N. (2014). A review on the basics of building energy estimation. Renewable and Sustainable Energy Reviews, Elsevier, 31, 53–60.
Gibson, B. R., Rogers, T. T., and Zhu, X. (2013). Human semi-supervised learning. topics in cognitive science, 5(1), 132–172. http://doi.org/10.1111/tops.12010
Galiotto, N., Heiselberg , P., and Knudstrup, M.A. (2015). Integrated renovation process overcoming barriers to sustainable renovation. Journal of Architectural Engineering, 21(3): 04015007.
Gallagher, C. V., Bruton, K., Leahy, K., and O’Sullivan, D. T. J. (2018). The suitability of machine learning to minimize uncertainty in the measurement and verification of energy savings. Energy and Buildings, Elsevier B.V., 158, 647–655.
Garnier, A., Eynard, J., Caussanel, M., and Grieu, S. (2015). Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings. Applied Soft Computing Journal, Elsevier B.V., 37, 847–862.
Gauthier, J. (2014). Conditional generative adversarial nets for convolutional face generation. Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester 2014, 5, 2.
gbXML site. Retrieved 7, 7, 2015, from http://www.gbXML.org/. last reviewed, 12/12/2017.
GenOpt, Retrieved 20, 1, 2018, from https://simulationresearch.lbl.gov/GO/
Goedkoop M, De Schryver A, Oele M, Durksz S, de R. D. (2016). Introduction to LCA with SimaPro Colophon, (PRé Consultants).
Goldberg DE. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company.
Goldberg, A. B., Zhu, X., Furger, A., and Xu, J. (2011). OASIS : Online Active SemI-Supervised Learning. Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI), pp. 362–367.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. 1–9.
González, A., Bouillard, P., Román, and Consolación, A. (2015). TCS Matrix: Evaluation of optimal energy retrofitting strategies. 7th International Conference on Sustainability in Energy and Buildings, Energy Procedia, 88, 101–110.
Gosavi, A. (2015). Simulation-based optimization: Parametric optimization techniques and reinforcement learning, Springer New York Heidelberg Dordrecht London. In Operations Research/Computer Science Interfaces Series, 55.
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., and Schmidhuber, J. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855–868.
Giebeler, G., Krause, H., Fisch, R., Musso, F., Lenz, B., and Rudolphi, A. (2009). Refurbishment manual: maintenance, conversions, extensions. Walter de Gruyter.
Gulrajani, I., Kumar, K., Ahmed, F., Taiga, A. A., Visin, F., Vazquez, D., and Courville, A. (2016). PixelVAE: A latent variable model for natural images. Conference paper at the International Conference on Learning Representations (ICLR), 1–9.
Gurney K. (2005). An introduction to neural networks, [Book]. London, UK, Taylor and Francis.
Häkkinen, T. and Kiviniemi, A. (2008). Sustainable building and BIM, Melbourne, Australia.
Hammond, G., Jones, C., Lowrie, F., and Tse, P. (2008). Inventory of carbon and energy: ICE. Bath Sustainable Energy Research Team. Department of Mechanical Engineering, University of Bath.
Harris, R. (2012). Introduction to Decision Making, Part 1, VirtualSalt, Retrieved 11, 1, 2016, from http://www.virtualsalt.com/crebook5.htm
Harish, V. S. K. V., and Kumar, A. (2016). A review on modeling and simulation of building energy systems. Renewable and Sustainable Energy Reviews, Elsevier Ltd, 56, 1272–1292.
He, Y., Liu, R., Li, H., Wang, S., and Lu, X. (2017). Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory. Applied Energy, 185, 254–266. http://doi.org/10.1016/j.apenergy.2016.10.079
Honkela, T., Birlutiu, A., and Heskes, T. (2011). Artificial neural networks and machine learning-- ICANN 2011, Proceedings of the 21st international conference on artificial neural networks - Volume Part II.
Holland J. H. (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence [Book], USA: University of Michigan Press.
Horsey, H., Fleming, K., Ball, B. and Long, N., (2016). Achieving actionable results from available inputs: metamodels take building energy simulations one step further. Golden, CO. (United States), National Renewable Energy Laboratory (NREL).
Howland, A. (2013). The residential energy map : Catalyzing energy efficiency through remote energy assessments and improved data access. MIT Department of Urban Studies and Planning.
Huang, J., Lv, H., Gao, T., Feng, W., Chen, Y., and Zhou, T. (2014). Thermal properties optimization of envelope in energy-saving renovation of existing public buildings. Energy and Buildings, Elsevier B.V., 75, 504–510.
Huang, Y., and Niu, J. (2015). Optimal building envelope design based on simulated performance: History, current status and new potentials. Energy and Buildings, Elsevier B.V., 117, 387–398.
Huang, H., Chen, L., and Hu, E. (2015). A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study. Building and Environment, Elsevier Ltd, 89, 203–216.
Institute for Building Environment and Energy Conservation. (2013). Total energy use in buildings analysis and evaluation methods. Program on Energy in Buildings and Communities, 132.
IPCC, Intergovernmental Panel on Climate Change. (2007). Climate Change 2007: The Physical Science Basis. Switzerland, WMO.
International Alliance for Interoperability (IAI). (2006). IFC model view definition. Retrieved 1, 11, 2016, from http://www.blis-project.org/IAI-MVD/.
Islam, H., Jollands, M., Setunge, S., Haque, N., and Bhuiyan, M. A. (2015). Life cycle assessment and life cycle cost implications for roofing and floor designs in residential buildings. Energy and Buildings, Elsevier B.V., 104, 250–263.
ISO 14040 Environmental management - Life Cycle Assessment - Principles and Framework. International Organization for Standardization 2006.
ISO International Organization for Standardization. ISO 16739:2013, Retrieved 15, 5, 2016, from https://www.iso.org/standard/51622.html.
Itard, L. and Meijer , F. (2008). Towards a sustainable Northern European housing stock (Vol. 22). Amsterdam: Delft Center for Sustainable Urban Areas.
Iyer-Raniga, U., and Wong, J. P. C. (2012). Evaluation of whole life cycle assessment for heritage buildings in Australia. Building and Environment, Elsevier Ltd, 47(1), 138–149.
Jalaei, F. and Jrade, A. (2014). Integrating Building Information Modeling (BIM) and energy analysis tools with green building certification system to conceptually design sustainable buildings. Journal of Information Technology in Construction,19, 494-519.
JCHS. (2019). Improving America’s housing 2019 report, Joint Center for Housing Studies of Harvard University (JCHS), Harvard Graduate School Of Design, Harvard Kennedy School, https://www.jchs.harvard.edu/sites/default/files/Harvard_JCHS_Improving_Americas_Housing_2019.pdf
Jin, Q., and Overend, M. (2012). Façade renovation for a public building based on a whole-life value approach. First Building Simulation and Optimization Conference (September), 417–424.
Jokisalo, J., and Kurnitski, J. (2007). Performance of EN ISO 13790 utilization factor heat demand calculation method in a cold climate. Energy and Buildings, 39(2), 236–247.
Juan, Y.K., Gao, P., and Wang, J. (2010). A hybrid decision support system for sustainable office building renovation and energy performance improvement. Energy and Buildings, 42, 290–297.
Junnila, S. (2004). The environmental impact of an office building throughout its life cycle. Helsinki University of Technology.
Karras, T. (2018). Progressive growing of GANs for improved quality, stability, and variation. Published as a conference paper at the International Conference on Learning Representations (ICLR)2018, 1–26.
Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., and Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, Elsevier Ltd, 45(7), 1683–1697.
Kalogirou, S. A., and Bojic, M. (2000). Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy, 25(5), 479–491.
Kelly, J. D. (2016). Disaggregation of domestic smart meter energy data. A thesis submitted in part fulfillment of the requirements for the degree of Doctor of Philosophy in Computing of the University of London and the Diploma of Imperial College.
Kibert, C. J. (2008). Sustainable construction: green building design and delivery. New Jersey, Wiley, Hoboken.
Kim, W., Jeon, Y., and Kim, Y. (2016). Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method. Applied Energy, Elsevier Ltd, 162, 666–674.
Kim, K. S., Lee, S., and Huang, K. (2018). A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting. Big Data Analytics, Big Data Analytics, 1–17.
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M. (2016). Improving Variational Inference with Inverse Autoregressive Flow.
Kingma, D. P., and Welling, M. (2014). Auto-Encoding Variational Bayes. arXiv : 1312 . 6114v10, (Ml), 1–14.
Kleijnen J. P. C. (1987). Statistical tools for simulation practitioners [Book].New York, Marcel Dekker.
Kolokotsa, D., Rovas, D., Kosmatopoulos, E., and Kalaitzakis, K. (2011). A roadmap towards intelligent net zero- and positive-energy buildings. Solar Energy, 85(12), 3067–3084.
Konstantinou, T. (2014). Façade refurbishment toolbox. Delft University of Technology, Faculty of Architecture and The Build Environment.
Kumar S. (2008). Interoperability between Building Information Modeling (BIM) and energy analysis programs, MSc thesis, University of southern California, USA.
Kunang, Y. N., Nurmaini, S., Stiawan, D., Zarkasi, A., and Jasmir, F. (2019). Automatic features extraction using autoencoder in intrusion detection system. Proceedings of 2018 International Conference on Electrical Engineering and Computer Science, ICECOS 2018, IEEE, 17, 219–224.
Längkvist, M., Karlsson, L., and Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42(1), 11–24.
Lasvaux, S., Habert, G., Peuportier, B., and Chevalier, J. (2015). Comparison of generic and product-specific Life Cycle Assessment databases: application to construction materials used in building LCA studies. International Journal of Life Cycle Assessment, 20(11), 1473–1490.
Lecun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Lee J. H. (2007). Optimization of indoor climate conditioning with passive and active methods using GA and CFD. Build Environ, 42(9), 3333-40.
Li, C., Ding, Z., Zhao, D., Yi, J., and Zhang, G. (2017). Building energy consumption prediction: An extreme deep learning approach. Energies, 10(10), 1–20.
Li, X., and Wu, X. (2015). Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4520–4524.
Li, X., Tan, H., and Rackes, A. (2015). Carbon footprint analysis of student behavior for a sustainable university campus in China. Journal of Cleaner Production, 106, 97–108. http://doi.org/10.1016/j.jclepro.2014.11.084
Lin, S. (2018). Machine learning models for high-dimensional biomedical data. A dissertation presented in partial fulfillment of the requirements for the degree Doctor of Philosophy in ARIZONA STATE University.
Loonen, R., Singaravel, S., Trčka, M., Cóstola, D., and Hensen, J. (2014). Simulation-based support for product development of innovative building envelope components. Automation in Construction, 45, 86-95.
Loucopoulos, P. and Zicari, R. (1992). Conceptual modeling, databases, and CASE an integrated view of information systems development. John Wiley and Sons.
Ma Z, Cooper P, Daly D, Ledo L (2012) Existing building retrofits methodology and state-of-the-art. Energy Build 55:889–902
Ma, J., and Cheng, J. C. P. (2016). Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology. Applied Energy, Elsevier Ltd, 183, 182–192.
Machairas, V., Tsangrassoulis, A., and Axarli, K. (2014). Algorithms for optimization of building design: A review. Renewable and Sustainable Energy Reviews, 31(1364), 101–112.
MacKay, D. J., (1995). Probable networks and plausible predictions a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems, Volume 6(3), pp. 469-505.
MacKay, D.J.C. (1992). A practical Bayesian framework for backpropagation networks, Neural Computation, Vol. 4, 448–472.
Maile, T., Fischer, M., Haymaker, J., and Bazjanac, V. (2010). Formalizing approximations, assumptions, and simplifications to document limitations in building energy performance simulation. CIFE, Civil and Environmental Engineering Dept., Stanford University.
Magnier, L. (2008). Multiobjective optimization of building design using artificial neural networks and multiobjective evolutionary algorithms (Doctoral dissertation, Concordia University).
Magnier, L., and Haghighat, F. (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithms, and artificial neural network. Building and Environment, Elsevier Ltd, 45(3), 739–746.
Makhzani, A. (2018). Unsupervised representation learning with autoencoders. A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy, Department of Electrical and Computer Engineering, University of Toronto.
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015). Adversarial Autoencoders, arXiv preprint arXiv:1511.05644.
Marasco, D. E., and Kontokosta, C. E. (2016). Applications of machine learning methods to identifying and predicting building retrofit opportunities. Energy and Buildings, Elsevier B.V., 128, 431–441.
Mathieu, M., Couprie, C., and LeCun, Y. (2016). Deep multi-scale video prediction beyond mean square error. Conference paper at the International Conference on Learning Representations (ICLR), 1–14.
McKinsey Global Energy and Materials. (2009). Unlocking Energy Efficiency in the U.S. Economy: Executive Summary.
Melo, A. P., Cóstola, D., Lamberts, R., and Hensen, J. L. M. (2014). Development of surrogate models using artificial neural networks for building shell energy labeling. Energy Policy, Elsevier, 69, 457–466.
Mocanu, E., Nguyen, P. H., Gibescu, M., and Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, Elsevier Ltd, 6, 91–99.
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., and Slootweg, J. G. (2018). On-line building energy optimization using deep reinforcement learning. IEEE transactions on smart grid, 1–11.
Monteiro, C., Ramirez-Rosado, I., Fernandez-Jimenez, L., and Conde, P. (2016). Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market. Energies, 9(9), 721. http://doi.org/10.3390/en9090721
Naganathan, H., Chong, W. O., and Chen, X. (2016). Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches. Automation in Construction, Elsevier B.V., 72, 187–194.
Naganathan, H. (2017). Energy Analytics for Infrastructure: An Application to Institutional Buildings. A dissertation presented in partial fulfillment of the requirements for the Degree Doctor of Philosophy, Arizona State University.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., and Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1–22.
Natural Resources Canada. (2015). Survey of Commercial and Institutional Energy Use (SCIEU), Buildings, Retrieved from Natural Resources Canada. Retrieved 1, 8, 2016, from https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/data_e/databases.cfm
Natural Resources Canada (2016), energy efficiency trends in Canada 1990 to 2013, Retrieved 20, 04, 2020, from https://www.nrcan.gc.ca/energy/publications/19030
Nakayama H., Yun Y. and Yoon M. (2009). Sequential approximate multi objective optimization using computation intelligence. [Book]. Berlin: Springer-Verlag Berlin Heidelberg.
Neuhoff, K., Amecke, H., Novikova, A., and Stelmakh, K. (2011). Thermal efficiency retrofit of residential buildings: The German experience. CPI Report, Climate Policy Initiative.
Neto, A. H., and Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and Buildings, 40(12), 2169–2176.
Nguyen, A.-T., Reiter, S., and Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, Elsevier Ltd, 113, 1043–1058.
Nicolae, B., and George-Vlad, B. (2015). Life cycle analysis in refurbishment of the buildings as intervention practices in energy saving. Energy and Buildings, Elsevier B.V., 86, 74–85.
Ning, M., and Zaheeruddin, M. (2010). Neuro-optimal operation of a variable air volume HVAC&R system. Applied Thermal Engineering, Elsevier Ltd, 30(5), 385–399.
Ochsendorf, J., Norford, L. K., Brown, D., Durschlag, H., Hsu, S. L., Love, A., Santero, N., Swei, O., Webb, A., and Wildnauer, M. (2011). Methods, impacts, and opportunities in the concrete building life cycle. Research Report R11-01, Concrete and Sustainability Hub, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 119.
Ortiz, O., Castells, F., and Sonnemann, G. (2009). Sustainability in the construction industry: A review of recent developments based on LCA. Construction and Building Materials, Elsevier Ltd, 23(1), 28–39.
Osório, G. J., Matias, J. C. O., and Catalão, J. P. S. (2015). Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renewable Energy, 75, 301–307. http://doi.org/10.1016/j.renene.2014.09.058
Ouyang, J., Ge, J., and Hokao, K. (2009). Economic analysis of energy-saving renovation measures for urban existing residential buildings in China based on thermal simulation and site investigation. 37, 140–149.
Papalexopoulos, A. D., and Hesterberg, T. C. (1990). A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems, 5(4), 1535–1547. http://doi.org/10.1109/59.99410
Palonen, M., Hasan, A., and Siren, K. (2009). A genetic algorithm for optimization of building envelope and HVAC system parameters. 11th International IBPSA Conference, Building Simulation 2009, 159–166.
Paterakis, N. G., Mocanu, E., and Gibescu, M. (2017). Deep learning versus traditional machine learning methods for aggregated energy demand prediction. In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 1-6.
Peng, C. (2016). Calculation of a building’s life cycle carbon emissions based on Ecotect and building information modeling. Journal of Cleaner Production, Elsevier, 112, 453–465.
Penna, P., Prada, A., Cappelletti, F., and Gasparella, A. (2015). Multi-objective optimization of Energy Efficiency Measures in existing buildings. Energy and Buildings, Elsevier B.V., 95, 57–69.
Polinder, H., Schweiker, M., Aa, A. Van Der, Schakib-Ekbatan, K., Fabi, V., Andersen, R., Morishita, N., Wang, C., Corgnati, S., Heiselberg, P., Yan, D., Olesen, B., Bednar, T., and Wagner, A. (2013). Final Report Annex 53 - Occupant behavior and modeling (Separate Document Volume II). 153.
Pitt, B. D., and Kitschen, D. S. (1999). Application of data mining techniques to load profiling. Proceedings of the 21st International Conference on Power Industry Computer Applications Connecting Utilities PICA 99, 131–136.
Purdy, J., and Beausoleil-Morrison, I. (2001). The significant factors in modeling residential buildings. In Canmet Center for Technology, 7th International IBPSA Conf., Rio de Janiero.
Rana, M., and Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118–132. http://doi.org/10.1016/j.neucom.2015.12.004
Ranzato, A. (2009). Unsupervised learning of feature hierarchies, A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computer Science New York University.
Resch, B., Sagl, G., Törnros, T., Bachmaier, A., Eggers, J.-B., Herkel, S., Narmsara, S., and Gündra, H. (2014). GIS-Based Planning and modeling for renewable energy: challenges and future research avenues. ISPRS International Journal of Geo-Information, 3(2), 662–692.
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models, Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. JMLR: WandCP, 32.
Rossi, B., Marique, A. F., Glaumann, M., and Reiter, S. (2012). Life-cycle assessment of residential buildings in three different European locations, basic tool. Building and Environment, Elsevier Ltd, 51, 395–401.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016). Improved techniques for training GANs. 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 1–9.
Salimzadeh, N., Sharif, S. A., and Hammad, A. (2016). Visualizing and analyzing urban energy consumption: A critical review and case study. Construction Research Congress 2016, 1323–1331.
Säynäjoki, A., Heinonen, J., and Junnila, S. (2012). A scenario analysis of the life cycle greenhouse gas emissions of a new residential area. Environmental Research Letters, 7(3), 034037.
Schwartz, Y., Raslan, R., and Mumovic, D. (2015). Multi-objective genetic algorithms for the minimization of the life cycle carbon footprint and life cycle cost of the refurbishment of a residential complex’s envelope: a case study. SimAUD 15 Proceedings of the Symposium on Simulation for Architecture and Urban Design, 189–196.
Schwartz, Y., Raslan, R., and Mumovic, D. (2016). Implementing multi-objective genetic algorithm for life cycle carbon footprint and life cycle cost minimization: A building refurbishment case study. Energy, Elsevier Ltd., 97, 58–68.
Sharif, S. and Hammad, A. (2017). Simulation-based optimization of building renovation considering energy consumption and life cycle assessment. International Workshop on Computing in Civil Engineering (IWCCE). Seattle: University of Washington.
Sharif, S. A., and Hammad, A. (2018). Simulation-based multi-objective optimization of institutional building renovation considering energy consumption, life-cycle cost and life-cycle assessment. Journal of Building Engineering, Elsevier, 21, 429–445.
Sharif, S. A., and Hammad, A. (2019). Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA. Journal of Building Engineering, Elsevier, 25, 100790.
Singaravel, S., Geyer, P., and Suykens, J. (2017). Deep neural network architectures for component-based machine learning model in building energy predictions. Digital Proceedings of the 24th EG-ICE International Workshop on Intelligent Computing in Engineering 2017.
Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Song, K.-B., Baek, Y.-S., Hong, D. H., and Jang, G. (2005). Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Transactions on Power Systems, 20(1), 96–101. http://doi.org/10.1109/TPWRS.2004.835632
Statistics Canada (2008). Retrieved 1, 9, 2015, from https://www45.statcan.gc.ca/2008/cgco_2008_000-eng.htm
Steurer, R. and Hametner, M. (2010). Objectives and indicators in sustainable development strategies: similarities and variances across Europe. DOI: 10.1002/sd.501.
Straube, J. and Burnett, E. (2005). Building science for building enclosures [Book]. Westford, Mass, Building Science Press Inc.
Susie R. Wu and Defne Apul (2015) framework for integrating indoor air quality impacts into life cycle assessments of buildings and building-related products. Journal of Green Building: Winter 2015, Vol. 10, No. 1, pp. 127-149.
Szargut, J., Morris, D., and Steward, F. (1988). Exergy analysis of thermal, chemical, and metallurgical process. New York, Hemisphere Publishing.
Tabatabaee S, Weil B, Aksamija A. (2015). Negative life-cycle emissions growth rate through retrofit of existing institutional buildings. Prediction at ARCC 2015 Conference 2015:212–21.
Tardioli, G., Kerrigan, R., Oates, M. R., O`Donnell, J., and Finn, D. (2015). Prediction of building energy use in an urban case study using data driven approaches 14th Conference of International Building Performance Simulation Association, Hyderabad, India, 1877–1884.
TranSystems, and Pechan, E. H. (2012). The emissions and generation resource integrated database for 2012 (eGRID 2012) technical support document. U.S. Environmental Protection Agency, Springfield, VA.
Tuominen, P., Klobut, K., Tolman, A., Adjei, A., and de Best-Waldhober, M. (2012). Energy savings potential in buildings and overcoming market barriers inmember states of the European union. Energy Build, 51, 48-55.
U.E.P.A. (2011). U.E.P.A. 2011 Green building-basic information. Retrieved 1, 1, 2015, from http://www.epa.gov/greenbuilding/pubs/about.htm.
U.S. Environmental Protection Agency. (2011). Strategic sustainability performance plan. U.S. Environmental Protection Agency.
U.S. Department of Energy. (2012). Annual energy outlook 2012 with projections to 2035, Retrieved 5, 1, 2016, from the EIA website at www.eia.gov/forecasts/aeo
USGBC US Green Building Council (USGBC), LEED is green building Retrieved 5, 5, 2017, from https://new.usgbc.org/leed.
Vandenbroucke , M., Galle, W., De Temmerman, N., Debacker, W., and Paduart, A. (2015). Using life cycle assessment to inform decision-making for sustainable buildings. Buildings, 5(2), 536-559.
Verbeeck, G., and Hens, H. (2010). Life cycle inventory of buildings: A calculation method. Building and Environment, 45(4), 1037-1041.
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P. A. (2010). Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371–3408.
Wang, Z., and Srinivasan, R. S. (2017). A review of artificial intelligence-based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews, Elsevier Ltd, 75 (November 2016), 796–808.
Wang, W., Rivard, H., and Zmeureanu, R. (2005). An object-oriented framework for simulation-based green building design optimization with genetic algorithms. Advanced Engineering Informatics, 19, 5-23.
Wang, W., Zmeureanu, R., and Rivard, H. (2005). Applying multi-objective genetic algorithms in green building design optimization. Building and environment, 40(11), 1512-1525.
Wang, S., Yan, C., and Xiao, F. (2012). Quantitative energy performance assessment methods for existing buildings. Energy and Buildings, Elsevier B.V., 55, 873–888.
WCED. (1987). Our common future. World commission on environment and developmen. New York, Oxford.
Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., and Zhao, X. (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, Elsevier Ltd, 82, 1027–1047.
Wei, X., Kusiak, A., Li, M., Tang, F., and Zeng, Y. (2015). Multi-objective optimization of the HVAC (Heating, Ventilation, and Air Conditioning) system performance. Energy, Elsevier Ltd, 83, 294–306.
Wetter, M., and Wright, J. (2004). A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Building and Environment, 39(8), 989-999.
Wu, S. R., and Apul, D. (2015). Framework for integrating indoor air quality impacts into life cycle assessments of buildings and building-related products. Journal of Green Building, 10(1), 127-149.
Xu, X., and Wang, S. (2008). A mixed-mode building energy model for performance evaluation and diagnosis of existing buildings. Building Services Engineering Research and Technology, 29,1, 73–83.
Yang, Z., Zhang, H., Yuan, Z., and Oja, E. (2011). Kullback-Leibler divergence for nonnegative matrix factorization. In International Conference on Artificial Neural Networks, 250-257, Springer, Berlin, Heidelberg.
Yang, J., Rivard, H., and Zmeureanu, R. (2005a). Building energy prediction with adaptive artificial neural networks. Ninth International IBPSA Conference, 1401–1408.
Yang, J., Rivard, H., and Zmeureanu, R. (2005b). On-line building energy prediction using adaptive artificial neural networks. Energy and Buildings, 37(12), 1250–1259.
Yildiz, B., Bilbao, J. I., and Sproul, A. B. (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews, Elsevier Ltd, 73, 1104–1122.
Yong K. Cho; Fernanda Leite; Amir Behzadan; and Chao Wang, ed. (2019). Computing in civil engineering 2019 smart cities, sustainability, and resilience, American Society of Civil Engineers (ASCE), Atlanta.
Yu, Z., Haghighat, F., and Fung, B. C. M. (2016). Advances and challenges in building engineering and data mining applications for energy-efficient communities. Sustainable Cities and Society, Elsevier B.V., 25, 33–38.
Yu, Z. J. (2012). Mining hidden knowledge from measured data for improving building energy performance. A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy, Department of Building, Civil and Environmental Engineering, Concordia University, 176.
Yu, W., Li, B., Jia, H., Zhang, M., and Wang, D. (2015). Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, Elsevier B.V., 88, 135–143.
Zhang, X., Shen, J., Tang, L., Yang, T., Xia, L., Hong, Z., Wang, L., Wu, Y., Shi, Y., and Xu, P. (2015). Building Integrated Solar Thermal (BIST) technologies and their applications : A review of structural design and architectural integration. Fundamentals of Renewable Energy and Applications, 5(5), 1000182.
Zhao, H. (2011). Artificial intelligence models for large scale buildings energy consumption analysis, A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy, Laboratoire mathematiques appliquees aux systemes, Ecole Centrale Paris, 125.
Zhao, H. X., and Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, Elsevier Ltd., 16(6), 3586–3592.
Zhao, S., Song, J., and Ermon, S. (2017). Towards deeper understanding of variational autoencoding models. arXiv:1702.08658 [cs.LG].
Zhu, X., Ghahramani, Z., and Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International conference on Machine learning (ICML-03), 912-919.
Zhu, X., and Goldberg, A. B. (2009). Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. http://doi.org/10.2200/S00196ED1V01Y200906AIM006
Zhu, Y. (2006). Applying computer-based simulation to energy auditing: A case study. Energy and Buildings, 38(5), 421–428.