The Academic Perspective Procedia publishes Academic Platform symposiums papers as three volumes in a year. DOI number is given to all of our papers.
Publisher : Academic Perspective
Journal DOI : 10.33793/acperpro
Journal eISSN : 2667-5862
 A. Kylili and P. A. Fokaides, “European smart cities: The role of zero energy buildings,” Sustain. Cities Soc., vol. 15, pp. 86–95, Jul. 2015, doi: 10.1016/J.SCS.2014.12.003.
 R. J. Hafner, D. Elmes, and D. Read, “Promoting behavioural change to reduce thermal energy demand in households: A review,” Renew. Sustain. Energy Rev., vol. 102, pp. 205–214, Mar. 2019, doi: 10.1016/J.RSER.2018.12.004.
 H. Zhao and F. Magoulès, “A review on the prediction of building energy consumption | Elsevier Enhanced Reader.” https://reader.elsevier.com/reader/sd/pii/S1364032112001438?token=ED4964E3C6054F0AD8E72A9F4ACCFA9CB78B624951F020383A5ED6D562ED45628D765A46E13E585B5027745FC2D0BB6FandoriginRegion=eu-west-1andoriginCreation=20210725115410 (accessed Jul. 25, 2021).
 D. Vuarnoz and T. Jusselme, “Temporal variations in the primary energy use and greenhouse gas emissions of electricity provided by the Swiss grid,” Energy, vol. 161, pp. 573–582, Oct. 2018, doi: 10.1016/J.ENERGY.2018.07.087.
 A. Batish and A. Agrawal, “Building Energy Prediction for Early Design Stage Decision Support: A Review of Data-driven Techniques,” in Proceedings of Building Simulation 2019: 16th Conference of IBPSA, 2020, vol. 16, pp. 1514–1521, doi: 10.26868/25222708.2019.211032.
 W. Gao, J. Alsarraf, H. Moayedi, A. Shahsavar, and H. Nguyen, “Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms,” Appl. Soft Comput. J., vol. 84, p. 105748, Nov. 2019, doi: 10.1016/j.asoc.2019.105748.
 R. Kumar, R. K. Aggarwal, and J. D. Sharma, “Energy analysis of a building using artificial neural network: A review,” Energy Build., vol. 65, pp. 352–358, Oct. 2013, doi: 10.1016/J.ENBUILD.2013.06.007.
 J. S. Chou and D. K. Bui, “Modeling heating and cooling loads by artificial intelligence for energy-efficient building design,” Energy Build., vol. 82, pp. 437–446, Oct. 2014, doi: 10.1016/j.enbuild.2014.07.036.
 M. W. Ahmad, M. Mourshed, and Y. Rezgui, “Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption,” Energy Build., vol. 147, pp. 77–89, Jul. 2017, doi: 10.1016/J.ENBUILD.2017.04.038.
 K. Pervez Amber et al., “Energy Consumption Forecasting for University Sector Buildings,” doi: 10.3390/en10101579.
 K. Amasyali and N. M. El-Gohary, “A review of data-driven building energy consumption prediction studies,” Renewable and Sustainable Energy Reviews, vol. 81. Elsevier Ltd, pp. 1192–1205, Jan. 01, 2018, doi: 10.1016/j.rser.2017.04.095.
 T. Ahmad, H. Chen, Y. Guo, and J. Wang, “A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review,” Energy Build., vol. 165, pp. 301–320, Apr. 2018, doi: 10.1016/j.enbuild.2018.01.017.
 M. Bourdeau, X. qiang Zhai, E. Nefzaoui, X. Guo, and P. Chatellier, “Modeling and forecasting building energy consumption: A review of data-driven techniques,” Sustain. Cities Soc., vol. 48, p. 101533, Jul. 2019, doi: https://doi.org/10.1016/j.scs.2019.101533.
 T. Østergård, R. L. Jensen, and S. E. Maagaard, “Building simulations supporting decision making in early design - A review,” Renewable and Sustainable Energy Reviews, vol. 61. Elsevier Ltd, pp. 187–201, Aug. 01, 2016, doi: 10.1016/j.rser.2016.03.045.
 S. Seyedzadeh, F. P. Rahimian, I. Glesk, and M. Roper, “Machine learning for estimation of building energy consumption and performance: a review,” Visualization in Engineering, vol. 6, no. 1. 2018, doi: 10.1186/s40327-018-0064-7.
 A. Tsanas and A. Xifara, “UCI Machine Learning Repository: Energy efficiency Data Set,” https://archive.ics.uci.edu/, 2012. https://archive.ics.uci.edu/ml/datasets/energy+efficiency (accessed Jun. 12, 2021).
 A. Tsanas and A. Xifara, “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools,” Energy Build., vol. 49, pp. 560–567, Jun. 2012, doi: 10.1016/j.enbuild.2012.03.003.
 R. Alpar, Uygulamalı istatistik ve geçerlilik güvenirlilik: Spor, sağlık ve eğitim bilimlerinden örneklerle, 2nd ed. Ankara: Detay Yayıncılık, 2016.
 A. Zheng, Evaluating Machine Learning Models, 1st ed. 2015.
 B. Ataseven, “Yapay Sinir Ağları İle öngörü Modellemesi,” öneri Derg., vol. 10, no. 39, pp. 101–115, 2013.