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Artificial neural networks for the prediction of the energy consumption of a passive solar building

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  1. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
  2. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
  3. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
  4. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
  5. Keçebaş, Ali & Alkan, Mehmet Ali & Yabanova, İsmail & Yumurtacı, Mehmet, 2013. "Energetic and economic evaluations of geothermal district heating systems by using ANN," Energy Policy, Elsevier, vol. 56(C), pages 558-567.
  6. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  7. Marek Dudzik, 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
  8. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
  9. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
  10. Zhijian Liu & Hao Li & Guoqing Cao, 2017. "Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning," IJERPH, MDPI, vol. 14(8), pages 1-9, July.
  11. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  12. Yeo, In-Ae & Yee, Jurng-Jae, 2014. "A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artifi," Applied Energy, Elsevier, vol. 119(C), pages 99-117.
  13. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
  14. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
  15. Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.
  16. Kalogirou, Soteris A. & Florides, Georgios A. & Pouloupatis, Panayiotis D. & Christodoulides, Paul & Joseph-Stylianou, Josephina, 2015. "Artificial neural networks for the generation of a conductivity map of the ground," Renewable Energy, Elsevier, vol. 77(C), pages 400-407.
  17. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
  18. Kicsiny, R. & Nagy, J. & Szalóki, Cs., 2014. "Extended ordinary differential equation models for solar heating systems with pipes," Applied Energy, Elsevier, vol. 129(C), pages 166-176.
  19. Badescu, Viorel & Laaser, Nadine & Crutescu, Ruxandra, 2010. "Warm season cooling requirements for passive buildings in Southeastern Europe (Romania)," Energy, Elsevier, vol. 35(8), pages 3284-3300.
  20. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
  21. Guelpa, Elisa & Marincioni, Ludovica & Verda, Vittorio, 2019. "Towards 4th generation district heating: Prediction of building thermal load for optimal management," Energy, Elsevier, vol. 171(C), pages 510-522.
  22. Beccali, M. & Bonomolo, M. & Ciulla, G. & Lo Brano, V., 2018. "Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks," Energy, Elsevier, vol. 154(C), pages 466-476.
  23. Goh, S.L. & Chen, M. & Popović, D.H. & Aihara, K. & Obradovic, D. & Mandic, D.P., 2006. "Complex-valued forecasting of wind profile," Renewable Energy, Elsevier, vol. 31(11), pages 1733-1750.
  24. Jin Woo Moon & Kyung-Il Chin & Sooyoung Kim, 2013. "Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings," Energies, MDPI, vol. 6(8), pages 1-23, August.
  25. Sehrish Malik & DoHyeun Kim, 2018. "Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks," Energies, MDPI, vol. 11(5), pages 1-21, May.
  26. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
  27. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
  28. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
  29. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2014. "A novel dynamic modeling approach for predicting building energy performance," Applied Energy, Elsevier, vol. 114(C), pages 91-103.
  30. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
  31. Young Min Kim & Ki Uhn Ahn & Cheol Soo Park, 2016. "Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings," Sustainability, MDPI, vol. 8(6), pages 1-15, June.
  32. Donghyun Lee & Suna Kang & Jungwoo Shin, 2017. "Using Deep Learning Techniques to Forecast Environmental Consumption Level," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
  33. Thirugnanasambandam, Mirunalini & Iniyan, S. & Goic, Ranko, 2010. "A review of solar thermal technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 312-322, January.
  34. Yafei Zhao & Paolo Vincenzo Genovese & Zhixing Li, 2020. "Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI," Future Internet, MDPI, vol. 12(2), pages 1-18, February.
  35. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
  36. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
  37. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
  38. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
  39. Luyao Wang & Hong Fan & Yankun Wang, 2018. "Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network," Sustainability, MDPI, vol. 10(6), pages 1-19, May.
  40. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
  41. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
  42. Xu, Weijun & Gu, Ren & Liu, Youzhu & Dai, Yongwu, 2015. "Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China," Economic Modelling, Elsevier, vol. 45(C), pages 127-135.
  43. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
  44. Wu, Sheng-Ju & Shiah, Sheau-Wen & Yu, Wei-Lung, 2009. "Parametric analysis of proton exchange membrane fuel cell performance by using the Taguchi method and a neural network," Renewable Energy, Elsevier, vol. 34(1), pages 135-144.
  45. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  46. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
  47. Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
  48. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
  49. Colorado, D. & Ali, M.E. & García-Valladares, O. & Hernández, J.A., 2011. "Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution," Energy, Elsevier, vol. 36(2), pages 854-863.
  50. Jin Woo Moon & Ji-Hyun Lee & Sooyoung Kim, 2014. "Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes," Energies, MDPI, vol. 7(11), pages 1-21, November.
  51. Ahmadi, Mohammadali, 2023. "Data-driven approaches for predicting wax deposition," Energy, Elsevier, vol. 265(C).
  52. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
  53. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
  54. He, Bao-jie & Ye, Miao & Yang, Li & Fu, Xiang-Ping & Mou, Ben & Griffy-Brown, Charla, 2014. "The combination of digital technology and architectural design to develop a process for enhancing energy-saving: The case of Maanshan China," Technology in Society, Elsevier, vol. 39(C), pages 77-87.
  55. Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
  56. Jin Woo Moon & Sung Kwon Jung & Yong Oh Lee & Sangsun Choi, 2015. "Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms," Energies, MDPI, vol. 8(8), pages 1-18, August.
  57. Sung Kwon Jung & Youngchul Kim & Jin Woo Moon, 2020. "Performance Evaluation of Control Methods for PV-Integrated Shading Devices," Energies, MDPI, vol. 13(12), pages 1-21, June.
  58. Kneifel, Joshua & Webb, David, 2016. "Predicting energy performance of a net-zero energy building: A statistical approach," Applied Energy, Elsevier, vol. 178(C), pages 468-483.
  59. Sözen, Adnan & Arcaklıoğlu, Erol & Özalp, Mehmet & Çağlar, Naci, 2005. "Forecasting based on neural network approach of solar potential in Turkey," Renewable Energy, Elsevier, vol. 30(7), pages 1075-1090.
  60. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2009. "Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks," Applied Energy, Elsevier, vol. 86(9), pages 1442-1449, September.
  61. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
  62. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
  63. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
  64. Di Lullo, G. & Oni, A.O. & Kumar, A., 2023. "The development of complex engineering models using artificial neural network-based proxy models for life cycle assessments of energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
  65. Singh, Ramkishore & Lazarus, Ian J. & Souliotis, Manolis, 2016. "Recent developments in integrated collector storage (ICS) solar water heaters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 270-298.
  66. Ceylan, Ilhan & Aktas, Mustafa, 2008. "Modeling of a hazelnut dryer assisted heat pump by using artificial neural networks," Applied Energy, Elsevier, vol. 85(9), pages 841-854, September.
  67. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
  68. Moradi, G.R. & Dehghani, S. & Khosravian, F. & Arjmandzadeh, A., 2013. "The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield," Renewable Energy, Elsevier, vol. 50(C), pages 915-920.
  69. Gunasekar, N. & Mohanraj, M. & Velmurugan, V., 2015. "Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps," Energy, Elsevier, vol. 93(P1), pages 908-922.
  70. Suyang Zhou & Fenghua Zou & Zhi Wu & Wei Gu, 2019. "Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing," Energies, MDPI, vol. 12(13), pages 1-16, July.
  71. Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
  72. Rodríguez-Hidalgo, M.C. & Rodríguez-Aumente, P.A. & Lecuona, A. & Legrand, M. & Ventas, R., 2012. "Domestic hot water consumption vs. solar thermal energy storage: The optimum size of the storage tank," Applied Energy, Elsevier, vol. 97(C), pages 897-906.
  73. Şencan, Arzu & Yakut, Kemal A. & Kalogirou, Soteris A., 2006. "Thermodynamic analysis of absorption systems using artificial neural network," Renewable Energy, Elsevier, vol. 31(1), pages 29-43.
  74. Wang, Zhangyuan & Yang, Wansheng & Qiu, Feng & Zhang, Xiangmei & Zhao, Xudong, 2015. "Solar water heating: From theory, application, marketing and research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 68-84.
  75. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok, 2016. "Development of an integrated energy benchmark for a multi-family housing complex using district heating," Applied Energy, Elsevier, vol. 179(C), pages 1048-1061.
  76. Altan Dombaycı, Ömer & Gölcü, Mustafa, 2009. "Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey," Renewable Energy, Elsevier, vol. 34(4), pages 1158-1161.
  77. Li, Rui & Dai, Yanjun & Wang, Ruzhu, 2015. "Experimental investigation and simulation analysis of the thermal performance of a balcony wall integrated solar water heating unit," Renewable Energy, Elsevier, vol. 75(C), pages 115-122.
  78. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Yücesu, Serdar, 2005. "Performance parameters of an ejector-absorption heat transformer," Applied Energy, Elsevier, vol. 80(3), pages 273-289, March.
  79. Lazrak, Amine & Leconte, Antoine & Chèze, David & Fraisse, Gilles & Papillon, Philippe & Souyri, Bernard, 2015. "Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies," Applied Energy, Elsevier, vol. 158(C), pages 142-156.
  80. Bin Xu & Xiang Yuan, 2022. "A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
  81. Boukelia, T.E. & Ghellab, A. & Laouafi, A. & Bouraoui, A. & Kabar, Y., 2020. "Cooling performances time series of CSP plants: Calculation and analysis using regression and ANN models," Renewable Energy, Elsevier, vol. 157(C), pages 809-827.
  82. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
  83. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
  84. Cai, Yuan & Wang, Jian-zhou & Tang, Yun & Yang, Yu-chen, 2011. "An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherical ARTMAP network) neural network," Energy, Elsevier, vol. 36(2), pages 1340-1350.
  85. Selimefendigil, Fatih & Öztop, Hakan F., 2021. "Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks," Renewable Energy, Elsevier, vol. 172(C), pages 582-598.
  86. Lamnatou, Chr. & Mondol, J.D. & Chemisana, D. & Maurer, C., 2015. "Modelling and simulation of Building-Integrated solar thermal systems: Behaviour of the coupled building/system configuration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 178-191.
  87. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
  88. Coskun, C. & Oktay, Z. & Dincer, I., 2011. "Estimation of monthly solar radiation distribution for solar energy system analysis," Energy, Elsevier, vol. 36(2), pages 1319-1323.
  89. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
  90. ArcaklIoglu, Erol & Çavusoglu, Abdullah & Erisen, Ali, 2004. "Thermodynamic analyses of refrigerant mixtures using artificial neural networks," Applied Energy, Elsevier, vol. 78(2), pages 219-230, June.
  91. Shamsi, Meisam & Babazadeh, Reza, 2022. "Estimation and prediction of Jatropha cultivation areas in China and India," Renewable Energy, Elsevier, vol. 183(C), pages 548-560.
  92. Kalogirou, Soteris A. & Florides, Georgios A. & Pouloupatis, Panayiotis D. & Panayides, Ioannis & Joseph-Stylianou, Josephina & Zomeni, Zomenia, 2012. "Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration," Energy, Elsevier, vol. 48(1), pages 233-240.
  93. Yang, S. & Pilet, T.J. & Ordonez, J.C., 2018. "Volume element model for 3D dynamic building thermal modeling and simulation," Energy, Elsevier, vol. 148(C), pages 642-661.
  94. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.
  95. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
  96. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
  97. Wong, Ross Y.M. & Tso, C.Y. & Jeong, S.Y. & Fu, S.C. & Chao, Christopher Y.H., 2023. "Critical sky temperatures for passive radiative cooling," Renewable Energy, Elsevier, vol. 211(C), pages 214-226.
  98. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
  99. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
  100. Chandra, Yogender Pal & Matuska, Tomas, 2020. "Numerical prediction of the stratification performance in domestic hot water storage tanks," Renewable Energy, Elsevier, vol. 154(C), pages 1165-1179.
  101. Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  102. Wen, Hao & Sang, Song & Qiu, Chenhui & Du, Xiangrui & Zhu, Xiao & Shi, Qian, 2019. "A new optimization method of wind turbine airfoil performance based on Bessel equation and GABP artificial neural network," Energy, Elsevier, vol. 187(C).
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