[1] J. Ahn, D.H. Chung and S. Choa, Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model, Energy 151 (2018), 11–25.
[2] M.H. Alobaidi, F. Chebana and M.A. Meguid, Robust ensemble learning framework for day-ahead forecasting of household based energy consumption, Appl. Energy 212 (2018), 997–1012.
[3] F. Amara, K. Agbossou, Y. Dub´e, S. Kelouwani, A. Cardenas and S.S. Hosseini, A residual load modeling approach for household short-term load forecasting application, Energy Build. 187 (2019), 132–143.
[4] K.P. Amber and M.W. Ahmad, Intelligent techniques for forecasting electricity consumption of buildings, Energy 157 (2018), 886–893.
[5] F. Ascione, N. Bianco, C. De Stasio, G.M. Mauro and G.P. Vanoli, Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort, Energy Build. 111 (2016), 131–144.
[6] A.R. Baheri, M. Najafi, A. Azimi and C. Aghanajafi, A simplified model to predict and optimize the energy consumption of residential buildings in the cold climate regions of Iran, Energy Sources, Part A: Recovery, Util. Envir. Effects 2020 (2020), 1–19.
[7] B. Bektas Ekici and U. Teoman Aksoy, Prediction of building energy needs in the early stage of design by using ANFIS, Expert Syst. Appl. 38 (2011), no. 5, 5352–5358.
[8] M.R. Biswas, M.D. Robinson and N. Fumo, Prediction of residential building energy consumption: A neural network approach, Energy 117 (2016), 84–92.
[9] C. Buratti, E. Lascaro and D. Palladino, Building behavior simulation by means of artificial neural Nnetwork in summer conditions, Sustain. 6 (2014), no. 8, 5339–5353.
[10] A. Chabaud, J. Eynard and S. Grieu, A new approach to energy resources management in a grid-connected building equipped with energy production and storage systems: A case study in the south of France, Energy Build. 99 (2015), 9–31.
[11] V. Ciancio, S. Falasca, I. Golasi, G. Curci, M. Coppi and F. Salata, Influence of input climatic data on simulations of annual energy needs of a building: Energy Plus and WRF modeling for a case study in Rome (Italy), Energies 11 (2018), no. 10, 2835.
[12] N. Delgarm, B. Sajadi, F. Kowsary and S. Delgarm, Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO), Appl. Energy 170 (2016), 293–303.
[13] R.E. Edwards, J. New and L.E. Parker, Predicting future hourly residential electrical consumption: a machine learning case study, Energy Build. 49 (2012), 591–603.
[14] P. Hameed, Intelligent multi-objective optimization for building energy and comfort management, J. King Saud Univ. Engin. Sci. 30 (2018), no. 2, 195–204.
[15] J. Jang, J. Baek and S.B. Leigh, Prediction of optimum heating timing based on the artificial neural network byutilizing BEMS data, J. Build. Eng. 22 (2019), 66–74.
[16] S. Lee, S. Jung and J. Lee, A Prediction model based on an artificial neural network for user-based building energy consumption in South Korea, Energies 12 (2019), no. 4, 608.
[17] X. Li, J. Wen and E.W. Bai, Developing a whole building cooling energy forecasting model for online operation optimization using proactive system identification, Appl. Energy 164 (2016), 69–88.
[18] S. Malik and D.H. Kim, Prediction-learning algorithm for efficient energy consumption in smart buildings based on particle regeneration and velocity boost in particle swarm optimization neural networks, Energies 11 (2018), no. 5, 1–22.
[19] E. Mocanu, P.H. Nguyen, W.L. Kling and M. Gibescu, Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning, Energy Build. 116 (2016), 646-655.
[20] S. Naji, A. Keivani, S.Shamshirband, U.J. Alengaram, M.Z. Jumaat, Z. Mansor and M. Lee, Estimating buildingenergy consumption using extreme learning machine method, Energy 97 (2016), 506-516.
[21] S. Naji, Sh. Shamshirband, H. Basser, U. Afram Keivanid, J. Alengaram, M. Zamin Jumaat and D. Petkovic, Application of adaptive neuro-fuzzy methodology for estimating building energy consumption, Renew. Sustain. Energy Rev. 53 (2016), 1520-1528.
[22] A.T. Nguyen, S. Reiter and P. Rigo, A review of simulation-based optimization methods applied to building performance analysis, Applied Energy, 113 (2014), 1043–1058.
[23] M. Nilashi, M. Dalvi-Esfahani, O. Ibrahim, K. Bagherifard, A. Mardani and N. Zakuan, A soft computing method for the prediction of the energy performance of residential buildings, Meas. J. Int. Meas. Confed. 109 (2017), 268–280.
[24] Q. Qiao, A. Yunusa-Kaltungo and E. Rodger, Towards developing a systematic knowledge trend for building energy consumption prediction, J. Build. Engin. 35 (2021), 101967.
[25] M. Raatikainena, M. Pekka, S. Kauko and L. Mikko, Intelligent analysis of energy consumption in school buildings, Appl. Energy 165 (2016), 416–429.
[26] R. Kumar, R.K. Aggarwal and J.D. Sharma Energy analysis of a building using artificial neural network: A review, Energy Build. 65 (2013), 352-358.
[27] C. Tian, C. Li, G. Zhang and Y. Lv, Data drove parallel prediction of building energy consumption using generative adversarial nets, Energy Build. 186 (2019), 230–243.
[28] I. Ullah, R. Ahmad and D.H. Kim, A prediction mechanism of energy consumption in residential buildings using hidden Markov model, Energies 11 (2018), no. 2, 1–20.
[29] J. Yang, H. Rivard and R. Zmeureanu, On-line building energy prediction using adaptive artificial neural networks, Energy Build. 37 (2005), no. 12, 1250–1259.
[30] W. Yu, B. Li, Y. Lei and M. Liu, Analysis of a residential building energy consumption demand model, Energies 4 (2011), no. 3, 475–487.
