How to cite this paper
Abdelkader, E., Al-Sakkaf, A & Ahmed, R. (2020). A comprehensive comparative analysis of machine learning models for predicting heating and cooling loads.Decision Science Letters , 9(3), 409-420.
Refrences
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Afsordegan, A., Sanchez, M., Agell, N., Zahedi, S., & Cremades, L. V. (2016). Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives, International Journal of Environmental Science and Technology, 13, 1419–1432.
Akande, O., Odeleye, D., Coday, A., & Bescos, C. (2015). Energy Performance and Improvement Potentials for Selected Heritage Building Adaptation in England. British Journal of Environment and Climate Change, 5(3), 189–201.
Alarcon, B., Aguado, A., Manga, R. & Josa, A. (2011). A Value Function for Assessing Sustainability: Application to Industrial Buildings, Sustainability, 3, 35-50.
Al-Sakkaf, A., Zayed, T., Bagchi, A., & Mohammed Abdelkader, E. (2019). Sustainability Rating Tool and Rehabilitation Model for Heritage Buildings. CSCE Annual Conference 2019, Laval, Canada.
Aranda, A., Ferreira, G., Mainar-Toledo, M.D, Scarpellini, S., & Sastresa, S. L. (2012). Multiple regression models to predict the annual energy consumption in the Spanish banking sector . Energy and Buildings, 49, 380-387.
Asuncion, A. & Newman, D. J. (2007). UCI Machine Learning Repository. University of California, Irvine. Retrieved from https://archive.ics.uci.edu/ml/index.php.
Attar, I., Naili, N., Khalifa, N., Hazami, M., & Farhat, A. (2013). Parametric and numerical study of a solar system for heating a greenhouse equipped with a buried exchanger. Energy Conversion and Management, 70, 163–173.
Cuadrado, J., Zubizarreta, M., Pelaz, B., & Marcos, I. (2015). Methodology to assess the environmental sustainability of timber structures, Construction and Building Materials, 86, 149-158.
Fahmy, M., Mahdy, M. M., & Nikolopoulou, M. (2014). Prediction of future energy consumption reduction using GRC envelope optimization for residential buildings in Egypt. Energy and Buildings, 70, 186–193.
Fumo, N., & Biswas, M. A. (2015). Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews, 47, 332–343.
Hygh, J. S., DeCarolis, J. F., Hill, D. B., & Ranji Ranjithan, S. (2012). Multivariate regression as an energy assessment tool in early building design. Building and Environment, 57, 165–175.
Kohestani, K. H. & Hassanlourad, M. (2016). “Modeling the Mechanical Behavior of Carbonate Sands Using Artificial Neural Networks and Support Vector Machines”. International Journal of Geomechanics, 16(1), 1–9.
Li, H. X., Zhang, L., Mah, D. & Yu, H. (2017). An integrated simulation and optimization approach for reducing CO 2 emissions from on-site construction process in cold regions. Energy and Buildings, 138, 666-675.
Lu, W., Chu, H., & Zhang, Z. (2015). Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province , China. Journal of Water Supply: Research and Technology, 64(1), 95–104.
Mahmoud, S., Zayed, T., & Fahmy, M. (2019). Development of sustainability assessment tool for existing buildings. Sustainable Cities and Society, 44, 99–119.
Marzouk, M. & Mohammed Abdelkader, E. (2019a). A hybrid fuzzy-optimization method for modeling construction emissions. Decision science letters, 9(1), 1-20.
Modaresi, F., Araghinejad, S., & Ebrahimi, K. (2017). A Comparative Assessment of Artificial Neural Network , Generalized Regression Neural Network , Least-Square Support Vector Regression , and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions. Water Resources Management, 32(1), 243–258.
Mohammed Abdelkader, E., Marzouk, M., & Zayed, T. (2019b). An Optimization-Based Methodology for the Definition of Amplitude Thresholds of the Ground Penetrating. Soft Computing, 23(22), 12063-12086.
Mohammed Abdelkader, E., Marzouk, M., and Zayed, T. (2019c). Mapping Ground Penetrating Radar Amplitudes using Artificial Neural Network and Multiple Regression Analysis Methods. International Journal of Strategic Decision Sciences, 10(2), 84-106.
Marzouk, M., Mohammed Abdelkader, E., & Al-Gahtani, K. (2017). Building information modeling-based model for calculating direct and indirect emissions in construction projects. Journal of cleaner production, 152, 351-363.
Mousa, M., Luo, X., & McCabe, B. (2016). Utilizing BIM and Carbon Estimating Methods for Meaningful Data Representation. Procedia Engineering, 145, 1242-1249.
Nazari, A., Rajeev, P., and Sanjayan, J. G. (2015). Offshore pipeline performance evaluation by different artificial neural networks approaches. Measurement, 76, 117–128.
Park, S., Inman, D. J., Lee, J., & Yun, C. (2008). “Piezoelectric Sensor-Based Health Monitoring of Railroad Tracks Using a Two-Step Support Vector Machine Classifier”. Journal of Infrastructure Systems, 14(1), 80–88.
Pinar, E., Paydas, K., Seckin, G., Akilli, H., Sahin, B., & Cobaner, M. (2010). Advances in Engineering Software Artificial neural network approaches for prediction of backwater through arched bridge constrictions. Advances in Engineering Software, 41(4), 627–635.
Poel, B., van Cruchten, G., & Balaras, C. A. (2007). Energy performance assessment of existing dwellings. Energy and Buildings, 39(4), 393–403.
Radhi, H., Sharples, S., & Fikiry, F. (2013). Will multi-facade systems reduce cooling energy in fully glazed buildings? A scoping study of UAE buildings. Energy and Buildings, 56, 179–188.
Ranjith, S., Setunge, S., Gravina, R., & Venkatesan, S. (2013). Deterioration Prediction of Timber Bridge Elements Using the Markov Chain. Journal of Performance of Constructed Facilities, 27(3), 319–325.
Tsanas, A. & Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49, 560-567.
United Nation. (2005). Kyoto Protocol to enter into force on 16 February 2005. Oil, Gas & Energy Law (OGEL), 3(1). Retrieved from http://www.ogel.org/article.asp?key=1804.
Ürge-Vorsatz, D., Harvey, L. D. D., Mirasgedis, S., & Levine, M. D. (2007). Mitigating CO2 emissions from energy use in the world’s buildings. Building Research and Information, 35(4), 379–398.
Vallabhaneni, V., & Maity, D. (2011). Application of Radial Basis Neural Network on Damage Assessment of Structures. Procedia Engineering, 14, 3104–3110.
Wang, L., Zeng, Y., & Chen, T. (2015). Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Systems With Applications, 42(2), 855–863.
Wong, J. K. W., Li, H., Wang, H., Huang, T., Luo, E., & Li, V. (2013). Toward low-carbon construction processes: The visualisation of predicted emission via virtual prototyping technology. Automation in Construction, 33, 72–78.
Yildirim, N., & Bilir, L. (2017). Evaluation of a hybrid system for a nearly zero energy greenhouse. Energy Conversion and Management, 148, 1278–1290.
Yu, F., and Xu, X. (2014). A Short-term Load Forecasting Model of Natural Gas Based on Optimized Genetic Algorithm and Improved BP Neural Network. Applied Energy, 134, 102–113.
Afsordegan, A., Sanchez, M., Agell, N., Zahedi, S., & Cremades, L. V. (2016). Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives, International Journal of Environmental Science and Technology, 13, 1419–1432.
Akande, O., Odeleye, D., Coday, A., & Bescos, C. (2015). Energy Performance and Improvement Potentials for Selected Heritage Building Adaptation in England. British Journal of Environment and Climate Change, 5(3), 189–201.
Alarcon, B., Aguado, A., Manga, R. & Josa, A. (2011). A Value Function for Assessing Sustainability: Application to Industrial Buildings, Sustainability, 3, 35-50.
Al-Sakkaf, A., Zayed, T., Bagchi, A., & Mohammed Abdelkader, E. (2019). Sustainability Rating Tool and Rehabilitation Model for Heritage Buildings. CSCE Annual Conference 2019, Laval, Canada.
Aranda, A., Ferreira, G., Mainar-Toledo, M.D, Scarpellini, S., & Sastresa, S. L. (2012). Multiple regression models to predict the annual energy consumption in the Spanish banking sector . Energy and Buildings, 49, 380-387.
Asuncion, A. & Newman, D. J. (2007). UCI Machine Learning Repository. University of California, Irvine. Retrieved from https://archive.ics.uci.edu/ml/index.php.
Attar, I., Naili, N., Khalifa, N., Hazami, M., & Farhat, A. (2013). Parametric and numerical study of a solar system for heating a greenhouse equipped with a buried exchanger. Energy Conversion and Management, 70, 163–173.
Cuadrado, J., Zubizarreta, M., Pelaz, B., & Marcos, I. (2015). Methodology to assess the environmental sustainability of timber structures, Construction and Building Materials, 86, 149-158.
Fahmy, M., Mahdy, M. M., & Nikolopoulou, M. (2014). Prediction of future energy consumption reduction using GRC envelope optimization for residential buildings in Egypt. Energy and Buildings, 70, 186–193.
Fumo, N., & Biswas, M. A. (2015). Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews, 47, 332–343.
Hygh, J. S., DeCarolis, J. F., Hill, D. B., & Ranji Ranjithan, S. (2012). Multivariate regression as an energy assessment tool in early building design. Building and Environment, 57, 165–175.
Kohestani, K. H. & Hassanlourad, M. (2016). “Modeling the Mechanical Behavior of Carbonate Sands Using Artificial Neural Networks and Support Vector Machines”. International Journal of Geomechanics, 16(1), 1–9.
Li, H. X., Zhang, L., Mah, D. & Yu, H. (2017). An integrated simulation and optimization approach for reducing CO 2 emissions from on-site construction process in cold regions. Energy and Buildings, 138, 666-675.
Lu, W., Chu, H., & Zhang, Z. (2015). Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province , China. Journal of Water Supply: Research and Technology, 64(1), 95–104.
Mahmoud, S., Zayed, T., & Fahmy, M. (2019). Development of sustainability assessment tool for existing buildings. Sustainable Cities and Society, 44, 99–119.
Marzouk, M. & Mohammed Abdelkader, E. (2019a). A hybrid fuzzy-optimization method for modeling construction emissions. Decision science letters, 9(1), 1-20.
Modaresi, F., Araghinejad, S., & Ebrahimi, K. (2017). A Comparative Assessment of Artificial Neural Network , Generalized Regression Neural Network , Least-Square Support Vector Regression , and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions. Water Resources Management, 32(1), 243–258.
Mohammed Abdelkader, E., Marzouk, M., & Zayed, T. (2019b). An Optimization-Based Methodology for the Definition of Amplitude Thresholds of the Ground Penetrating. Soft Computing, 23(22), 12063-12086.
Mohammed Abdelkader, E., Marzouk, M., and Zayed, T. (2019c). Mapping Ground Penetrating Radar Amplitudes using Artificial Neural Network and Multiple Regression Analysis Methods. International Journal of Strategic Decision Sciences, 10(2), 84-106.
Marzouk, M., Mohammed Abdelkader, E., & Al-Gahtani, K. (2017). Building information modeling-based model for calculating direct and indirect emissions in construction projects. Journal of cleaner production, 152, 351-363.
Mousa, M., Luo, X., & McCabe, B. (2016). Utilizing BIM and Carbon Estimating Methods for Meaningful Data Representation. Procedia Engineering, 145, 1242-1249.
Nazari, A., Rajeev, P., and Sanjayan, J. G. (2015). Offshore pipeline performance evaluation by different artificial neural networks approaches. Measurement, 76, 117–128.
Park, S., Inman, D. J., Lee, J., & Yun, C. (2008). “Piezoelectric Sensor-Based Health Monitoring of Railroad Tracks Using a Two-Step Support Vector Machine Classifier”. Journal of Infrastructure Systems, 14(1), 80–88.
Pinar, E., Paydas, K., Seckin, G., Akilli, H., Sahin, B., & Cobaner, M. (2010). Advances in Engineering Software Artificial neural network approaches for prediction of backwater through arched bridge constrictions. Advances in Engineering Software, 41(4), 627–635.
Poel, B., van Cruchten, G., & Balaras, C. A. (2007). Energy performance assessment of existing dwellings. Energy and Buildings, 39(4), 393–403.
Radhi, H., Sharples, S., & Fikiry, F. (2013). Will multi-facade systems reduce cooling energy in fully glazed buildings? A scoping study of UAE buildings. Energy and Buildings, 56, 179–188.
Ranjith, S., Setunge, S., Gravina, R., & Venkatesan, S. (2013). Deterioration Prediction of Timber Bridge Elements Using the Markov Chain. Journal of Performance of Constructed Facilities, 27(3), 319–325.
Tsanas, A. & Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49, 560-567.
United Nation. (2005). Kyoto Protocol to enter into force on 16 February 2005. Oil, Gas & Energy Law (OGEL), 3(1). Retrieved from http://www.ogel.org/article.asp?key=1804.
Ürge-Vorsatz, D., Harvey, L. D. D., Mirasgedis, S., & Levine, M. D. (2007). Mitigating CO2 emissions from energy use in the world’s buildings. Building Research and Information, 35(4), 379–398.
Vallabhaneni, V., & Maity, D. (2011). Application of Radial Basis Neural Network on Damage Assessment of Structures. Procedia Engineering, 14, 3104–3110.
Wang, L., Zeng, Y., & Chen, T. (2015). Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Systems With Applications, 42(2), 855–863.
Wong, J. K. W., Li, H., Wang, H., Huang, T., Luo, E., & Li, V. (2013). Toward low-carbon construction processes: The visualisation of predicted emission via virtual prototyping technology. Automation in Construction, 33, 72–78.
Yildirim, N., & Bilir, L. (2017). Evaluation of a hybrid system for a nearly zero energy greenhouse. Energy Conversion and Management, 148, 1278–1290.
Yu, F., and Xu, X. (2014). A Short-term Load Forecasting Model of Natural Gas Based on Optimized Genetic Algorithm and Improved BP Neural Network. Applied Energy, 134, 102–113.