Amber, K. P., Ahmad, R., Aslam, M. W., Kousar, A., Usman, M., & Khan, M. S. (2018). Intelligent techniques for forecasting electricity consumption of buildings. Energy, 157, pp. 886–893. doi: 10.1016/j.energy.2018.05.155.
Baldigara, T., & Koic, M. (2015). Modelling occupancy rates in croatian hotel industry. International Journal of Business Administration, 6(3), pp. 121–131. doi: 10.5430/ijba.v6n3p121.
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(6), pp. 1055–1064. doi: 10.1016/j.buildenv.2007.02.014.
Calis, G., Atalay, S. D., Kuru, M., & Mutlu, N. (2017). Forecasting occupancy for demand driven HVAC operations using time series analysis. Journal of Asian Architecture and Building Engineering, 16(3), pp. 655–660. doi: 10.3130/jaabe.16.655.
Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, pp. 198–213. doi: 10.1016/j.enbuild.2016.06.089.
D’Amico, A., Panno, D., Giuseppina, C., & Ferrari, S. (2019). Building energy demand assessment through heating degree days: The importance of a climatic dataset. Applied Energy, 242(December 2018), pp. 1285–1306. doi: 10.1016/J.APENERGY.2019.03.167.
Dombayci, Ö. A. (2007). The environmental impact of optimum insulation thickness for external walls of buildings. Building and Environment, 42(11), pp. 3855–3859. doi: 10.1016/j.buildenv.2006.10.054.
Durmayaz, A., & Kadioglu, M. (2003). Heating energy requirements and fuel consumptions in the biggest city centers of Turkey. Energy Conversion and Management, 44(7), pp. 1177–1192. doi: 10.1016/S0196-8904(02)00097-3.
Durmayaz, A., Kadioglu, M., & En, Z. (2000). An application of the degree-hours method to estimate the residential heating energy requirement and fuel consumption in Istanbul. Energy, 25(12), pp. 1245–1256. doi: 10.1016/S0360-5442(00)00040-2.
Elizbarashvili, M., Chartolani, G., & Khardziani, T. (2018). Variations and trends of heating and cooling degree-days in Georgia for 1961–1990 year period. Annals of Agrarian Science, 16(2), pp. 152–159. doi: 10.1016/j.aasci.2018.03.004.
EU Strategy for Heating and Cooling. (2019). Journal of Chemical Information and Modeling. Available at: https://ec.europa.eu/energy/en/topics/energy-efficiency/heating-and-cooling.
Fan, J. L., Hu, J. W., & Zhang, X. (2019). Impacts of climate change on electricity demand in China: An empirical estimation based on panel data. Energy, 170, pp. 880–888. doi: 10.1016/j.energy.2018.12.044.
Idchabani, R., Garoum, M., & Khaldoun, A. (2015). Analysis and mapping of the heating and cooling degree-days for Morocco at variable base temperatures. International Journal of Ambient Energy, 36(4), pp. 190–198. doi: 10.1080/01430750.2013.842497.
IPCC (2007). In Climate Change 2007: Synthesis Report. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden P.J. & Hanson C.E. (eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Kam, H. J., Sung, J. O., & Park, R. W. (2010). Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthcare Informatics Research, 16(3), pp. 158–165. doi: 10.4258/hir.2010.16.3.158.
Kohler, M., Blond, N., & Clappier, A. (2016). A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France). Applied Energy, 184, pp. 40–54. doi: 10.1016/j.apenergy.2016.09.075.
Kurekci, N. A. (2016). Determination of optimum insulation thickness for building walls by using heating and cooling degree-day values of all Turkey’s provincial centers. Energy and Buildings, 118(825), pp. 197–213. doi: 10.1016/j.enbuild.2016.03.004.
Lewis, C. D. (1982). Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. Butterworth Scientific, London, Boston.
Meng, Q., & Mourshed, M. (2017). Degree-day based non-domestic building energy analytics and modelling should use building and type specific base temperatures. Energy and Buildings, 155, pp. 260–268. doi: 10.1016/j.enbuild.2017.09.034.
Mourshed, M. (2012). Relationship between annual mean temperature and degree-days. Energy and Buildings, 54, pp. 418–425. doi: 10.1016/j.enbuild.2012.07.024.
NCSS data analysis. (2019). Time Series and Forecasting Methods in NCSS. Available at https:www.ncss.com/software/ncss/time-series-and-forecasting-in-ncss/.
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), pp. 2169–2176. doi: 10.1016/j.enbuild.2008.06.013.
OrtizBeviá, M. J., Sánchez-López, G., Alvarez-Garcìa, F. J., & Ruizdeelvira, A. (2012). Evolution of heating and cooling degree-days in Spain: Trends and interannual variability. Global and Planetary Change, 92–93, pp. 236–247. doi: 10.1016/j.gloplacha.2012.05.023.
Özmen, A., Yılmaz, Y., & Weber, G. W. (2018). Natural gas consumption forecast with MARS and CMARS models for residential users. Energy Economics, 70, pp. 357–381. doi: 10.1016/j.eneco.2018.01.022.
Sarak, H., & Satman, A. (2003). The degree-day method to estimate the residential heating natural gas consumption in Turkey: A case study. Energy, 28(9), pp. 929–939. doi: 10.1016/S0360-5442(03)00035-5.
Statistical Office of the European Union. (2019). Energy Statistics - Cooling and Heating Degree Days. Available at ec.europa.eu/eurostat/cache/metadata/fr/nrg_chdd_esms.htm.
Trigaux, D., Oosterbosch, B., De Troyer, F., Allacker, K. (2017). A design tool to assess the heating energy demand and the associated financial and environmental impact in neighbourhoods. Energy and Buildings, 152, pp. 516–523. doi: 10.1016/j.enbuild.2017.07.057.
Wu, J., Reddy, T. A., & Claridge, D. (1992). Statistical Modeling of Daily Energy Consumption in Commercial Buildings Using Multiple Regression and Principal Component Analysis. In: Proceedings of the Eighth Symposium on Improving Building Systems in Hot and Humid Climates. Dalla, Texas, pp. 155–164. doi: 10.20595/jjbf.19.0_3.
Yu, J., Yang, J., Tian, L., & Liao, D. (2009). A study on optimum insulation thicknesses of external walls in hot summer and cold winter zone of China. Applied Energy, 86(11), pp. 2520–2529. doi: 10.1016/j.apenergy.2009.03.010.