Abrahamse, W., Steg, L., Vlek, C., Rothengatter, T. (2007). The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. Journal of Environmental Psychology, 27(4), 265–276. https://doi.org/10.1016/j.jenvp.2007.08.002.
Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F, Abdullah, H., Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102–109. https://doi.org/10.1016/j.rser.2014.01.069.
Ahn, J., Cho, S., Chung, D.H. (2017). Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands. Applied Energy, 190, 222–231. https://doi.org/10.1016/j.apenergy.2016.12.155.
Alam, A.G., Baek, C.I., Han, H. (2016). Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments. Applied Mechanics and Materials, 819, 541–545. https://doi.org/10.4028/www.scientific.net/AMM.819.541.
Antanasijević, D., Pocajt, V., Ristić, M., Perić-Grujić, A. (2015). Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy, 84, 816–824. https://doi.org/10.1016/j.energy.2015.03.060.
Arambula Lara, R., Pernigotto, G., Cappelletti, F., Romagnoni, P., Gasparella, A. (2015). Energy audit of schools by means of cluster analysis. Energy and Buildings, 95, 160–171. https://doi.org/10.1016/j.enbuild.2015.03.036.
Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., Vanoli, G.P. (2014). A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance. Energy and Buildings, 88, 78–90. https://doi.org/10.1016/j.enbuild.2014.11.058.
Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., Vanoli, G.P. (2016). Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality. Applied Energy, 174, 37–68. https://doi.org/10.1016/j.apenergy.2016.04.078.
Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., Vanoli, G.P. (2017). Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy, 118, 999–1017. https://doi.org/10.1016/j.energy.2016.10.126.
Aydinalp, M., Ugursal, V.I., Fung, A.S. (2002). Modeling of the appliance, lighting and space-cooling energy consumption in the residential sector using neural networks. Applied Energy, 71(2), 87–110. https://doi.org/10.1016/j.apenergy.2003.12.006.
Aydinalp, M., Ugursal, V.I., Fung, A.S. (2004). Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks. Applied Energy, 79(2), 159–178. https://doi.org/10.1016/j.apenergy.2003.12.006.
Azadeh, A., Ghaderi, S.F., Sohrabkhani, S. (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management, 49(8), 2272–2278. https://doi.org/10.1016/j.enconman.2008.01.035.
Azadeh, M.A., & Sohrabkhani, S. (2006). Annual electricity consumption forecasting with Neural Network in high energy consuming industrial sectors of Iran, vol. 49. In Proceedings of the ieee international conference on industrial technology. https://doi.org/10.1109/ICIT.2006.372572. IEEE, Pergamon, (pp. 2166–2171).
Beccali, M., Ciulla, G., Lo Brano, V., Galatioto, A., Bonomolo, M. (2017). Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the nonresidential building stock in Southern Italy. Energy, 137, 1201–1218. https://doi.org/10.1016/j.energy.2017.05.200.
Bell, M. (2004). Energy Efficiency in Existing Buildings: the Role of Building Regulations. In Cobra 2004 proc. of the rics foundation construction and building research conference. Retrieved from http://www.leedsbeckett.ac.uk/as/cebe/projects/cobra04-1.pdf, (p. 16).
Benedetti, M., Cesarotti, V., Introna, V., Serranti, J. (2016). Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study. Applied Energy, 165, 60–71. https://doi.org/10.1016/j.apenergy.2015.12.066.
Ben-Nakhi, A.E., & Mahmoud, M.A. (2004). Cooling load prediction for buildings using general regression neural networks. Energy Conversion and Management, 45(13–14), 2127–2141. https://doi.org/10.1016/j.enconman.2003.10.009.
Biswas, M.R., Robinson, M.D., Fumo, M.D. (2016). Prediction of residential building energy consumption: A neural network approach. Energy, 117, 84–92. https://doi.org/10.1016/j.energy.2016.10.066.
Bukkapatnam, S.T., & Cheng, C. (2010). Forecasting the evolution of nonlinear and nonstationary systems using recurrencebased local Gaussian process models. Physical Review E Statistical, Nonlinear, and Soft Matter Physics, 82(5), 56206. https://doi.org/10.1103/PhysRevE.82.056206.
Buratti, C., Barbanera, M., Palladino, D. (2014). An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks. Applied Energy, 120, 125–132. https://doi.org/10.1016/j.apenergy.2014.01.053.
Burkhart, M.C., Heo, Y., Zavala, V.M. (2014). Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach. Energy and Buildings, 75, 189–198. https://doi.org/10.1016/j.enbuild.2014.01.048.
Cheng, M.-Y., & Cao, M.-T. (2014). Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines. Applied Soft Computing, 22, 178–188. https://doi.org/10.1016/j.asoc.2014.05.015.
Chung, W (2011). Review of building energy-use performance benchmarking methodologies. Applied Energy, 88(5), 1470–1479. https://doi.org/10.1016/j.apenergy.2010.11.022.
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., Glazer, J. (2001). EnergyPlus: Creating a newgeneration building energy simulation program. Energy and Buildings, 33(4), 319–331. https://doi.org/10.1016/S0378-7788(00)00114-6.
Deb, C., Eang, L.S., Yang, J., Santamouris, M. (2016). Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy and Buildings, 121, 284–297. https://doi.org/10.1016/j.enbuild.2015.12.050.
Dombayci, Ö.A. (2010). The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli-Turkey. Advances in Engineering Software, 41(2), 141–147. https://doi.org/10.1016/j.advengsoft.2009.09.012.
Dong, B., Cao, C., Lee, S.E. (2005). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37(5), 545–553. https://doi.org/10.1016/j.enbuild.2004.09.009.
Dounis, A.I., & Caraiscos, C. (2009). Advanced control systems engineering for energy and comfort management in a building environment A review. Renewable and Sustainable Energy Reviews, 13(6), 1246–1261. https://doi.org/10.1016/j.rser.2008.09.015.
Du, Z., Fan, B., Jin, X., Chi, J. (2013). Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Building and Environment, 73, 1–11. https://doi.org/10.1016/j.buildenv.2013.11.021.
Edwards, R.E., New, J., Parker, L.E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, 49, 591–603. https://doi.org/10.1016/j.enbuild.2012.03.010.
Ekici, B.B., & Aksoy, U.T. (2009). Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software, 40(5), 356–362. https://doi.org/10.1016/j.advengsoft.2008.05.003.
Ferlito, S., Atrigna, M., Graditi, G., De Vito, S., Salvato, M., Buonanno, A., Di Francia, G. (2015). Predictive models for building’s energy consumption: An Artificial Neural Network (ANN) approach. In 2015 xviii aisem annual conference. https://doi.org/10.1109/AISEM.2015.7066836, (pp. 1–4).
Filippin, C. (2000). Benchmarking the energy efficiency and greenhouse gases emissions of school buildings in central Argentina. Building and Environment, 35(5), 407–414. https://doi.org/10.1016/S0360-1323(99)00035-9.
Gaitani, N., Lehmann, C., Santamouris, M., Mihalakakou, M., Patargias, P. (2010). Using principal component and cluster analysis in the heating evaluation of the school building sector. Applied Energy, 87(6), 2079–2086. https://doi.org/10.1016/j.apenergy.2009.12.007.
Gao, X., & Malkawi, A. (2014). A new methodology for building energy performance benchmarking: An approach based on intelligent clustering algorithm. Energy and Buildings, 84, 607–616. https://doi.org/10.1016/j.enbuild.2014.08.030.
Gath, I., & Geva, A. (1989). Unsupervised optimal fuzzy clustering. IEEE Transactions on pattern analysis and machine intelligence, 11(7), 773–780. https://doi.org/10.1109/34.192473.
Gers, F., & Schmidhuber, J. (2000). Recurrent nets that time and count, vol. 3. In Ieee-inns-enns international joint conference on neural networks. https://doi.org/10.1109/IJCNN.2000.861302. IEEE, (pp. 189–194).
González, P.A., & Zamarreño, J.M. (2005). Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy and Buildings, 37(6), 595–601. https://doi.org/10.1016/j.enbuild.2004.09.006.
Grosicki, E., Abed-Meraim, E., Hua, Y. (2005). A weighted linear prediction method for near-field source localization. IEEE Transactions on Signal Processing, 53(10 I), 3651–3660. https://doi.org/10.1109/TSP.2005.855100.
Harpham, C., & Dawson, C.W. (2006). The effect of different basis functions on a radial basis function network for time series prediction: a comparative study. Neurocomputing, 69(16), 2161–2170. https://doi.org/10.1016/j.neucom.2005.07.010.
He, H, Menicucci, D., Caudell, T., Mammoli, A. (2011). Real-time fault detection for solar hot water systems using adaptive resonance theory neural networks. In Asme 2011 5th international conference on energy sustainability, volume es2011, Washington, DC. Retrieved from http://proceedings.asmedigitalcollection.asme.org/data/conferences/es2011/70415/1059_1.pdf, Washington.
Heo, Y., Choudhary, R., Augenbroe, G.A. (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47, 550–560. https://doi.org/10.1016/j.enbuild.2011.12.029.
Heo, Y., & Zavala, V.M. (2012). Gaussian process modeling for measurement and verification of building energy savings. Energy and Buildings, 53, 7–18. https://doi.org/10.1016/j.enbuild.2012.06.024.
Hong, S.M., Paterson, G., Burman, E., Steadman, P., Mumovic, D. (2014). A comparative study of benchmarking approaches for non-domestic buildings: Part 1 Top-down approach. International Journal of Sustainable Built Environment, 2(2), 119–130. https://doi.org/10.1016/j.ijsbe.2014.04.001.
Hong, S.-M., Paterson, G., Mumovic, D., Steadman, P. (2014a). Improved benchmarking comparability for energy consumption in schools. Building Research & Information, 42(1), 47–61. https://doi.org/10.1080/09613218.2013.814746.
Hong, S.M., Paterson, G., Mumovic, D., Steadman, P. (2014b). Improved benchmarking comparability for energy consumption in schools. Building Research and Information, 42(1), 47–61. https://doi.org/10.1080/09613218.2013.814746.
Hong, T., Koo, C., Kim, J., Lee, M., Jeong, K. (2015). A review on sustainable construction management strategies for monitoring, diagnosing and retrofitting the building’s dynamic energy performance: Focused on the operation and maintenance phase. Applied Energy, 155, 671–707. https://doi.org/10.1016/j.apenergy.2015.06.043.
Hou, Z., & Lian, Z. (2009). An application of support vector machines in cooling load prediction. In Intelligent systems and applications, 2009. isa, vol. 2. https://doi.org/10.1109/IWISA.2009.5072707. IEEE, (pp. 1–4).
Hou, Z., Lian, Z., Yao, Y., Yuan, X. (2006). Cooling-load prediction by the combination of rough set theory and an artiticial neural-network based on data-fusion technique. Applied Energy, 83(9), 1033–1046. https://doi.org/10.1016/j.apenergy.2005.08.006.
Huang, H., Chen, L., Hu, E. (2015). A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings. Energy and Buildings, 97, 86–97. https://doi.org/10.1016/j.enbuild.2015.03.045.
Hygh, J.S., DeCarolis, J.F., Hill, D.B., Ranjithan, S.R. (2012). Multivariate regression as an energy assessment tool in early building design. Building and Environment, 57, 165–175. https://doi.org/10.1016/j.buildenv.2012.04.021.
Jain, R.K., Smith, K.M., Culligan, P.J., Taylor, J.E. (2014). Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123, 168–178. https://doi.org/10.1016/j.apenergy.2014.02.057.
Jalori, S., & Reddy, T.A. (2015). A new clustering method to identify outliers and diurnal schedules from building energy interval data. ASHRAE Transactions, 121, 33–44. Retrieved from http://auroenergy.com/wp-content/uploads/2016/05/2015SaurabhASHRaE-TransClustering.pdf.
Jiang, X., Dong, B., Xie, L., Sweeney, L. (2010). Adaptive Gaussian Process for Short-Term Wind Speed Forecasting. In ECAI. Retrieved from http://www.ece.tamu.edu/le.xie/papers/Xie-AdaptiveGaussian-2010.pdf, (pp. 661–666).
Jinhu, L., Xuemei, L., Lixing, D., Liangzhong, J. (2010). Applying principal component analysis and weighted support vector machine in building cooling load forecasting. In International conference on computer and communication technologies in agriculture engineering, vol. 1. https://doi.org/10.1109/CCTAE.2010.5543476. IEEE, (pp. 434–437).
Jung, H.C., Kim, J.S., Heo, H. (2015). Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach. Energy and buildings, 90, 76–84. Elsevier B.V. https://doi.org/10.1016/j.enbuild.2014.12.029.
Kalogirou, S., & Bojic, M. (2000). Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy, 25(5), 479–491. https://doi.org/10.1016/S0360-5442(99)00086-9.
Kalogirou, S., Florides, G., Neocleous, C., Schizas, C. (2001). Estimation of Daily Heating and Cooling Loads Using Artificial Neural Networks. Naples. Retrieved from http://ktisis.cut.ac.cy/bitstream/10488/883/3/C41-CLIMA2001.pdf.
Kalogirou, S., Lalot, S., Florides, G., Desmet, B. (2008). Development of a neural network-based fault diagnostic system for solar thermal applications. Solar Energy, 82(2), 164–172. https://doi.org/10.1016/j.solener.2007.06.010.
Kalogirou, S.A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1–2), 17–35. https://doi.org/10.1016/S0306-2619(00)00005-2.
Karatasou, S., Santamouris, M., Geros, V. (2006). Modeling and predicting building’s energy use with artificial neural networks: Methods and results. Energy and Buildings, 38(8), 949–958. https://doi.org/10.1016/j.enbuild.2005.11.005.
Kavousian, A., & Rajagopal, R. (2014). Data-Driven Benchmarking of Building Energy Efficiency Utilizing Statistical Frontier Models. Journal of Computing in Civil Engineering, 28(1), 79–88. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000327.
Kelly, S., Crawford-Brown, D., Pollitt, M.G. (2012). Building performance evaluation and certification in the UK: Is SAP fit for purpose?Renewable and Sustainable Energy Reviews, 16(9), 6861–6878. https://doi.org/10.1016/j.rser.2012.07.018.
Khayatian, F., Sarto, L., Dall‘O’, G. (2016). Application of neural networks for evaluating energy performance certificates of residential buildings. Energy and Buildings, 125, 45–54. https://doi.org/10.1016/j.enbuild.2016.04.067.
Kialashaki, A., & Reisel, J.R. (2013). Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks. Applied Energy, 108, 271–280. https://doi.org/10.1016/j.apenergy.2013.03.034.
Kialashaki, A., & Reisel, J.R. (2014). Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States. Energy, 76, 749–760. https://doi.org/10.1016/j.energy.2014.08.072.
Kumar, R., Aggarwal, R.K., Sharma, J.D. (2013). Energy analysis of a building using artificial neural network: A review. Energy and Buildings, 65, 352. https://doi.org/10.1016/j.enbuild.2013.06.007.
Lai, F., Magoulès, F., Lherminier, F. (2008). Vapnik’s learning theory applied to energy consumption forecasts in residential buildings. International Journal of Computer Mathematics, 85(10), 1563–1588. https://doi.org/10.1080/00207160802033582.
Leung, H., Lo, T., Wang, S. (2001). Prediction of Noisy Chaotic Time Series Using an Optimal Radial Basis Function Neural Network. IEEE Transactions on Neural Networks, 12(5), 1163–1172. https://doi.org/10.1109/72.950144.
Li, K., Hu, C., Liu, G., Xue, W. (2015). Building’s electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy and Buildings, 108, 106–113. https://doi.org/10.1016/j.enbuild.2015.09.002.
Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A. (2009a). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86(10), 2249–2256. https://doi.org/10.1016/j.apenergy.2008.11.035.
Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A. (2009b). Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Conversion and Management, 50(1), 90–96. https://doi.org/10.1016/j.enconman.2008.08.033.
Li, Q., Ren, P., Meng, Q. (2010). Prediction model of annual energy consumption of residential buildings. In 2010 international conference on advances in energy engineering. Retrieved from https://doi.org/10.1109/ICAEE.2010.5557576. IEEE, (pp. 223–226).
Li, X., Bowers, C.P., Schnier, T. (2010). Classification of energy consumption in buildings with outlier detection. IEEE Transactions on Industrial Electronics, 57(11), 3639–3644. https://doi.org/10.1109/TIE.2009.2027926.
Li, X., Ding, L., L, J., Xu, G., Li, J. (2010). A novel hybrid approach of KPCA and SVM for building cooling load prediction. In 3rd international conference on knowledge discovery and data mining, wkdd 2010. https://doi.org/10.1109/WKDD.2010.137, (pp. 522–526).
Li, X., Ding, L., Li, L. (2010). A novel building cooling load prediction based on SVR and SAPSO. In 3ca 2010 - 2010 international symposium on computer, communication, control and automation, vol. 1. https://doi.org/10.1109/3CA.2010.5533863, (pp. 528–532).
Li, Z., Han, Y., Xu, P. (2014). Methods for benchmarking building energy consumption against its past or intended performance: An overview, vol. 124. https://doi.org/10.1016/j.apenergy.2014.03.020.
Liang, J., & Du, R. (2007). Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method. International Journal of Refrigeration, 30(6), 1104–1114. https://doi.org/10.1016/j.ijrefrig.2006.12.012.
Lundin, M., Andersson, S., Ãstin, R. (2004). Development and validation of a method aimed at estimating building performance parameters. Energy and Buildings, 36(9), 905–914. https://doi.org/10.1016/j.enbuild.2004.02.005.
Ma, Z., Cooper, P., Daly, D., Ledo, L. (2012). Existing Building Retrofits : Methodology and State - of - the - Art. Energy and buildings, 55(12), 889–902. https://doi.org/10.1016/j.enbuild.2012.08.018.
MacArthur, J.W., Mathur, A., Zhao, J. (1989). On-line recursive estimation for load profile prediction. ASHRAE transactions, 95, 621–628. Retrieved from http://cat.inist.fr/?aModele=afficheN&cpsidt=6935287.
Magoulès, F., Zhao, H.x., Elizondo, D. (2013). Development of an RDP neural network for building energy consumption fault detection and diagnosis. Energy and Buildings, 62, 133–138. https://doi.org/10.1016/j.enbuild.2013.02.050.
Manfren, M., Aste, N., Moshksar, R. (2013). Calibration and uncertainty analysis for computer models - A meta-model based approach for integrated building energy simulation. Applied Energy, 103, 627–641. https://doi.org/10.1016/j.apenergy.2012.10.031.
Marszal, A.J., Heiselberg, P., Bourrelle, J.S., Musall, E., Voss, K., Sartori, I., Napolitano, A. (2011). Author’s personal copy Zero Energy Building A review of definitions and calculation methodologies Author’s personal copy. Energy and buildings, 43(4), 971–979. https://doi.org/10.1016/j.enbuild.2010.12.022.
Massana, J., Pous, C., Burgas, L., Melendez, J., Colomer, J. (2015). Short-term load forecasting in a non-residential building contrasting models and attributes. Energy and Buildings, 92, 322–330. https://doi.org/10.1016/j.enbuild.2015.02.007.
Mena, R., Rodríguez, F., Castilla, M., Arahal, M.R. (2014). A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy and Buildings, 82, 142–155. https://doi.org/10.1016/j.enbuild.2014.06.052.
Mihalakakou, G., Santamouris, M., Tsangrassoulis, A. (2002). On the energy consumption in residential buildings. Energy and Buildings, 34(7), 727–736. https://doi.org/10.1016/S0378-7788(01)00137-2.
Mousavi-Avval, S.H., Rafiee, S., Jafari, A., Mohammadi, A. (2011). Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach. Applied Energy, 88(11), 3765–3772. https://doi.org/10.1016/j.apenergy.2011.04.021.
Neto, A.H., & 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. https://doi.org/10.1016/j.enbuild.2008.06.013.
Nghiem, T.X., & Jones, C.N. (2017). Data-driven Demand Response Modeling and Control of Buildings with Gaussian Processes. In 2017 American control conference. https://doi.org/10.1145/1235.
Nikolaou, T., Kolokotsa, D., Stavrakakis, G., Apostolou, A., Munteanu, C. (2015). Review and State of the Art on Methodologies of Buildings’ Energy-Efficiency Classification. In Managing indoor environments and energy in buildings with integrated intelligent systems. https://doi.org/10.2174/97816080528511120101. Springer International Publishing, (pp. 13–31).
Noh, G., & Rajagopal, R. (2013). Data-driven forecasting algorithms for building energy consumption. In Sensors and smart structures technologies for civil, mechanical, and aerospace systems, vol. 8692. https://doi.org/10.1117/12.2009894. SPIE, San Diego, (p. 86920T).
Olofsson, T., & Andersson, S. (2001). Long-term energy demand predictions based on short-term measured data. Energy and Buildings, 33(2), 85–91. https://doi.org/10.1016/S0378-7788(00)00068-2.
Park, B., Messer, C.J., Urbanik II, T. (1998). Short-term freeway traffic volume forecasting using radial basis function neural network. Transportation Research Record: Journal of the Transportation Research Board, 1651, 1651, 39–47. https://doi.org/10.3141/1651-06.
Park, Y.-S., & Lek, S. (2016). Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling. In Developments in environmental modelling, (pp. 123–140): Wiley Online Library. https://doi.org/10.1016/B978-0-444-63623-2.00007-4.
Paudel, S., Elmtiri, M., Kling, W.L., Corre, O.L., Lacarrière, B. (2014). Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network. Energy and Buildings, 70, 81–93. https://doi.org/10.1016/j.enbuild.2013.11.051.
Pérez-Ortiz, J.A., Gers, F.A., Eck, D., Schmidhuber, J. (2003). Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks, 16(2), 241–250. https://doi.org/10.1016/S0893-6080(02)00219-8.
Petcharat, S., Chungpaibulpatana, S., Rakkwamsuk, P. (2012). Assessment of potential energy saving using cluster analysis: A case study of lighting systems in buildings. Energy and Buildings, 52, 145–152. https://doi.org/10.1016/j.enbuild.2012.06.006.
Pieri, S.P., Tzouvadakis, I., Santamouris, M. (2015). Identifying energy consumption patterns in the Attica hotel sector using cluster analysis techniques with the aim of reducing hotels’ CO2 footprint. Energy and Buildings, 94, 252–262. https://doi.org/10.1016/j.enbuild.2015.02.017.
Platon, R., Dehkordi, V.R., Martel, J. (2015). Hourly prediction of a building’s electricity consumption using case-based reasoning, artificial neural networks and principal component analysis. Energy and Buildings, 92, 10–18. https://doi.org/10.1016/j.enbuild.2015.01.047.
Popescu, D., Ungureanu, F., Hernández-Guerrero, A. (2009). Simulation models for the analysis of space heat consumption of buildings. Energy, 34(10), 1447–1453. https://doi.org/10.1016/j.energy.2009.05.035.
Pour Rahimian, F., Arciszewski, T., Goulding, J.S. (2014). Successful education for AEC professionals: case study of applying immersive gamelike virtual reality interfaces. Visualization in Engineering, 2(1), 4. https://doi.org/10.1186/2213-7459-2-4.
Rastogi, P., Polytechnique, E., Lausanne, F.D. (2017). Gaussian-Process-Based Emulators for Building Performance Simulation. In Building simulation 2017: The 15th international conference of ibpsa. Retrieved from https://infoscience.epfl.ch/record/252858/files/BS2017448.pdf. IBPSA, San Francisco.
Reynolds, D. (2015). Gaussian Mixture Models. Encyclopedia of biometrics, 827–832. https://doi.org/10.1007/978-1-4899-7488-4196.
Ruch, D., Chen, L., Haberl, J.S., Claridge, D.E. (1993). A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model. Journal of solar energy engineering, 115(2), 77. https://doi.org/10.1115/1.2930035.
Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., Pavlou, C., Doukas, P., Primikiri, E., Geros, V., Assimakopoulos, M.N., Mitoula, R., Zerefos, S. (2007). Using intelligent clustering techniques to classify the energy performance of school buildings. Energy and Buildings, 39(1), 45–51. https://doi.org/10.1016/j.enbuild.2006.04.018.
Shaikh, P.H., Nor, N.B.M., Nallagownden, P., Elamvazuthi, I., Ibrahim, T. (2014). A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renewable and Sustainable Energy Reviews, 34, 409–429. https://doi.org/10.1016/j.rser.2014.03.027.
Smarra, F., Jain, A., de Rubeis, T., Ambrosini, D., D’Innocenzo, A., Mangharam, R. (2018). Data-driven model predictive control using random forests for building energy optimization and climate control. https://doi.org/10.1016/J.APENERGY.2018.02.126.
Solomon, D.M., Winter, R.L., Boulanger, A.G., Anderson, R.N., Wu, L.L. (2011). Forecasting energy demand in large commercial buildings using support vector machine regression (Tech. Rep.)Retrieved from http://academiccommons.columbia.edu/catalog/ac:143154.
Srivastav, A., Tewari, A., Dong, B. (2013). Baseline building energy modeling and localized uncertainty quantification using Gaussian mixture models. Energy and Buildings, 65, 438–447. https://doi.org/10.1016/j.enbuild.2013.05.037.
The Energy Systems Research Unit (ESRU) (2011). ESP-r. Retrieved 2018-02-25, from http://www.esru.strath.ac.uk/Programs/ESP-r.htm.
Tso, G.K.F., & Yau, K.K.W. (2007). Predicting electricity energy consumption : A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761–1768. https://doi.org/10.1016/j.energy.2006.11.010.
University of Wisconsin-Madison (2015). A Transient Systems Simulation Program. Retrieved 31/02/2018, from http://sel.me.wisc.edu/trnsys/.
Wang, B., Xia, X., Zhang, J. (2014). A multi-objective optimization model for the life-cycle cost analysis and retrofitting planning of buildings. Energy and Buildings, 77, 227–235. https://doi.org/10.1016/j.enbuild.2014.03.025.
Wong, S., Wan, K.K., Lam, T.N. (2010). Artificial neural networks for energy analysis of office buildings with daylighting. Applied Energy, 87(2), 551–557. https://doi.org/10.1016/j.apenergy.2009.06.028.
Xing-ping, Z., & Rui, G.U. (2007). Electrical Energy Consumption Forecasting Based on Cointegration and a Support Vector Machine in China. In Wseas transactions on mathematics, vol. 6. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.533.9017&rep=rep1&type=pdf, (pp. 878–883).
Xuemei, L., Yuyan, D., Lixing, D., Liangzhong, J. (2010). Building cooling load forecasting using fuzzy support vector machine and fuzzy C-mean clustering. In Computer and communication technologies in agriculture engineering (cctae), 2010 international conference on, vol. 1. https://doi.org/10.1109/CCTAE.2010.5543577, (pp. 438–441).
Xuemei, L.X.L., Jin-hu, L.J.-h.L., Lixing, D.L.D., Gang, X.G.X., Jibin, L.J.L. (2009). Building Cooling Load Forecasting Model Based on LSSVM. Asia-Pacific Conference on Information Processing, 1, 55–58. https://doi.org/10.1109/APCIP.2009.22.
Yalcintas, M. (2006). An energy benchmarking model based on artificial neural network method with a case example for tropical climates. International Journal of Energy Research, 31(14), 1158–1174. https://doi.org/10.1002/er.1212.
Yalcintas, M., & Ozturk, U.A. (2007). An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database. International Journal of Energy Research, 31(4), 412–421. https://doi.org/10.1002/er.1232.
Yan, C.W., & Yao, J. (2010). Application of ANN for the prediction of building energy consumption at different climate zones with HDD and CDD. In Proceedings of the 2010 2nd International Conference on Future Computer and Communication, ICFCC 2010, Vol. 3 (Cdd). https://doi.org/10.1109/ICFCC.2010.5497626, (pp. 286–289).
Yang, I.-H., Yeo, M.-S., Kim, K.-W. (2003). Application of artificial neural network to predict the optimal start time for heating system in building. Energy Conversion and Management, 44(17), 2791–2809. https://doi.org/10.1016/S0196-8904(03)00044-X.
Yang, J., Ning, C., Deb, C., Zhang, F., Cheong, D., Lee, S.E., Sekhar, C., Tham, K.W. (2017). k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement. Energy and Buildings, 146, 27–37. https://doi.org/10.1016/j.enbuild.2017.03.071.
Yang, J., Rivard, H., Zmeureanu, R. (2005). On-line building energy prediction using adaptive artificial neural networks. Energy and Buildings, 37(12), 1250–1259. https://doi.org/10.1016/j.enbuild.2005.02.005x.
Yang, R., & Wang, L. (2013). Development of multi-agent system for building energy and comfort management based on occupant behaviors. Energy and Buildings, 56, 1–7. https://doi.org/10.1016/j.enbuild.2012.10.025.
Yokoyama, R., Wakui, T., Satake, R. (2009). Prediction of energy demands using neural network with model identification by global optimization. Energy Conversion and Management, 50(2), 319–327. https://doi.org/10.1016/j.enconman.2008.09.017.
Yu, Z., Fung, B.C., Haghighat, F., Yoshino, H., Morofsky, E. (2011). A systematic procedure to study the in uence of occupant behavior on building energy consumption. Energy and Buildings, 43(6), 1409–1417. Retrieved from https://doi.org/10.1016/j.enbuild.2011.02.002.
Zhang, Y., O’Neill, Z., Dong, B., Augenbroe, G. (2015a). Building and Environment, 86, 177. https://doi.org/10.1016/j.buildenv.2014.12.023.
Zhang, Y., O’Neill, Z., Dong, B., Augenbroe, G. (2015b). Comparisons of inverse modeling approaches for predicting building energy performance. Building and Environment, 86, 177–190. https://doi.org/10.1016/j.buildenv.2014.12.023.
Zhang, Y., O’Neill, Z., Wagner, T., Augenbroe, G. (2013). An inverse model with uncertainty quantification to estimate the energy performance of an office building. IBPSA Building Simulation, 614–621. Retrieved from http://www.ibpsa.org/proceedings/BS2013/p1410.pdf.
Zhang, Y.-m., & Qi, W.-g. (2009). Interval Forecasting for Heating Load Using Support Vector Regression and Error Correcting Markov Chains. In International conference on machine learning and cybernetics. https://doi.org/10.1109/ICMLC.2009.5212405, Hebei, (pp. 1106–1110).
Zhao, H.-x., & Magoulès, F. (2010). Parallel Support Vector Machines Applied to the Prediction of Multiple Buildings Energy Consumption. Journal of Algorithms & Computational Technology, 4(2), 231–249. https://doi.org/10.1260/1748-3018.4.2.231.
Zhao, H.-X., & Magoulès, F. (2012a). Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method. Journal of Algorithms & Computational Technology, 6(1), 59–77. https://doi.org/10.1260/1748-3018.6.1.59.
Zhao, H.X., & Magoulès, F. (2012b). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586–3592. https://doi.org/10.1016/j.rser.2012.02.049.