The task is to write a literature review about deep learning models for predicting building heating and cooling loads in arid climate, baised on three research gaps as explained below, and using the references attached at the end

 

 

Tuning deep learning models for predicting building heating and cooling loads in arid climate

Abstract

 

In —–, residential buildings consume more than half of the total energy. The energy consumption is even higher in regions located in the center of the country due to their arid climate. —— region, which is the seventh most populated region in the country, has an arid desert climate, known for its extremely cold winters and for extremely hot summers. To alleviate this problem, this study investigates the effect of using deep neural network models in predicting building heating and cooling loads with different tunnings of the models. The tuned models are constructed using a dataset generated by IES simulation software for —— city, the capital of the —— region. The efficiency of the constructed models is demonstrated using several performance measures. The outcomes of the research assist architects in heating and cooling loads of new energy-efficient design buildings in the pre-design stage.

 

 

Related work

Research gap

• Most of the current studies of energy consumption in buildings do not focus on building characteristics as predictors.

Examples of studies that have been excluded are [50]–[56], all of which were not based on building characteristics to predict energy consumption. Instead, they utilized historical data of energy consumption [53]–[56] or combined historical data with types of data like climate data [50]–[52]. In [50], the authors predicted HVAC energy consumption based on historical weather, consumption, and occupancy data; [51] predicted cooling load based on climate prediction parameters and daily and weekly energy usage ; [52] predicted the total energy consumption prediction based on energy consumption and climate data;[53] proposed an energy consumption prediction model based on historical energy consumption in kiloWatt (kW) in the period of December 2006 to November 2010 with one-minute interval;[54] suggested a model based on thermal energy consumption data with five minute-interval from a large number of smart meters

deployed in 12 buildings; [55] proposed a prediction model based on the hourly electric demand intensity in two buildings; while [56] predicted the hourly electric demand based on multi-dimensional attributes of 100 days.

 

-Paraphrase the highlighted text.

-Refer to the attached references as they mentioned in the text.

-Elaborated more based on the research gap (Most of the current studies of energy consumption in buildings do not focus on building characteristics as predictors.)

-and more studies which are not focusing on building characteristics to highlight the gap

 

 

• Most of the studies depend on one dataset for prediction published by Tsanas and Xifara [57].

A large proportion of papers (13 out of 33 papers) used the benchmarked energy efficiency dataset provided by Tsanas and Xifara [57], which contains 768 instances and is available at  https://archive.ics.uci.edu/ml/datasets/Energy+efficiency, with its first 25 records shown in Fig.  14. This dataset contains eight building characteristics as predictors for buildings’ energy consumption (X1-X8):  Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, Glazing Area, Distribution. The dataset contains two target variables (Y1 and Y2): Heating Load and Cooling Load.

 

-Paraphrase the highlighted text.

-Refer to the attached references as they mentioned in the text.

-Elaborated more based on the research gap(Most of the studies depend on one dataset for prediction published by Tsanas and Xifara [57])as this research using self-generated dataset

– and more studies which are not listed in the text(more about the ones which didn’t focus on building characteristic

 

• Most of the current studies of energy consumption models used machine learning methods, while deep learning rarely used.

In [38], a deep neural network was proposed for forecasting heating and cooling loads of buildings depending on various buildings’ parameters. A deep neural network with sensitivity analysis (DNN-SA) model was presented in [39] to forecast heating and cooling loads for a variety of structures, as shown in Fig.  21. The DNNs proposed in this research include multi-layer perceptron (MLP) networks, and all the models were developed using extensive testing with many layers. The sensitivity analysis was used as a post-processing technique to extract information from the trained model. The study reported in [58] proposed a two-layer and three-layer deep LSTM models for heating cooling prediction models. The results show that deep learning increases the model’s performance when compared to simple ANN models.

 

-Paraphrase the highlighted text.

-Refer to the attached references as they mentioned in the text.

-Elaborated more based on the research gap(Most of the current studies of energy consumption models used machine learning methods, with e deep learning rarely used.)

– only three studies which used deep learning based on building characteristics which means ts limited , explain more

 

 

[1] Y. H. Lin, M. Der Lin, K. T. Tsai, M. J. Deng, and H. Ishii, “Multi-objective optimization design of green building envelopes and air conditioning systems for energy conservation and CO2 emission reduction,” Sustain. Cities Soc., vol. 64, p. 102555, Jan. 2021, doi: 10.1016/j.scs.2020.102555.

[2] Y. Wang and C. Wei, “Design optimization of office building envelope based on quantum genetic algorithm for energy conservation,” J. Build. Eng., vol. 35, p. 102048, 2021, doi: https://doi.org/10.1016/j.jobe.2020.102048.

[3] J.-S. Chou and N.-T. Ngo, “Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns,” Appl. Energy, vol. 177, pp. 751–770, Sep. 2016, doi: 10.1016/j.apenergy.2016.05.074.

[4] A. González-Vidal, F. Jiménez, and A. F. Gómez-Skarmeta, “A methodology for energy multivariate time series forecasting in smart buildings based on feature selection,” Energy Build., vol. 196, pp. 71–82, Aug. 2019, doi: 10.1016/j.enbuild.2019.05.021.

[5] Y. Guo et al., “Machine learning-based thermal response time ahead energy demand prediction for building heating systems,” Appl. Energy, vol. 221, pp. 16–27, Jul. 2018, doi: 10.1016/j.apenergy.2018.03.125.

[6] F. Divina, M. García Torres, F. A. Goméz Vela, and J. L. Vázquez Noguera, “A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings,” Energies, vol. 12, no. 10, p. 1934, May 2019, doi: 10.3390/en12101934.

[7] G. Bode, T. Schreiber, M. Baranski, and D. Müller, “A time series clustering approach for Building Automation and Control Systems,” Appl. Energy, vol. 238, pp. 1337–1345, Mar. 2019, doi: 10.1016/j.apenergy.2019.01.196.

[8] S. Papadopoulos, B. Bonczak, and C. E. Kontokosta, “Pattern recognition in building energy performance over time using energy benchmarking data,” Appl. Energy, vol. 221, pp. 576–586, Jul. 2018, doi: 10.1016/j.apenergy.2018.03.079.

[9] J.-S. Chou and D.-S. Tran, “Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders,” Energy, vol. 165, pp. 709–726, Dec. 2018, doi: 10.1016/j.energy.2018.09.144.

[10] E. T. Maddalena, Y. Lian, and C. N. Jones, “Data-driven methods for building control — A review and promising future directions,” Control Eng. Pract., vol. 95, p. 104211, Feb. 2020, doi: 10.1016/j.conengprac.2019.104211.

[11] Z. Wang and Y. Chen, “Data-driven modeling of building thermal dynamics: Methodology and state of the art,” Energy Build., vol. 203, p. 109405, Nov. 2019, doi: 10.1016/j.enbuild.2019.109405.

[12] 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: 10.1016/j.scs.2019.101533.

[13] 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.

[14] K. Amasyali and N. M. El-Gohary, “A review of data-driven building energy consumption prediction studies,” Renew. Sustain. Energy Rev., vol. 81, pp. 1192–1205, Jan. 2018, doi: 10.1016/J.RSER.2017.04.095.

[15] D. Guyot, F. Giraud, F. Simon, D. Corgier, C. Marvillet, and B. Tremeac, “Overview of the use of artificial neural networks for energy‐related applications in the building sector,” Int. J. Energy Res., vol. 43, no. 13, p. er.4706, Jul. 2019, doi: 10.1002/er.4706.

[16] S. R. Mohandes, X. Zhang, and A. Mahdiyar, “A comprehensive review on the application of artificial neural networks in building energy analysis,” Neurocomputing, vol. 340, pp. 55–75, May 2019, doi: 10.1016/j.neucom.2019.02.040.

[17] J. Runge and R. Zmeureanu, “Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review,” Energies, vol. 12, no. 17, p. 3254, Aug. 2019, doi: 10.3390/en12173254.

[18] B. Yildiz, J. I. Bilbao, and A. B. Sproul, “A review and analysis of regression and machine learning models on commercial building electricity load forecasting,” Renew. Sustain. Energy Rev., vol. 73, pp. 1104–1122, Jun. 2017, doi: 10.1016/j.rser.2017.02.023.

[19] M. A. Mat Daut, M. Y. Hassan, H. Abdullah, H. A. Rahman, M. P. Abdullah, and F. Hussin, “Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review,” Renew. Sustain. Energy Rev., vol. 70, pp. 1108–1118, Apr. 2017, doi: 10.1016/j.rser.2016.12.015.

[20] H. Sha, P. Xu, Z. Yang, Y. Chen, and J. Tang, “Overview of computational intelligence for building energy system design,” Renew. Sustain. Energy Rev., vol. 108, pp. 76–90, Jul. 2019, doi: 10.1016/j.rser.2019.03.018.

[21] M. U. Mehmood, D. Chun, Zeeshan, H. Han, G. Jeon, and K. Chen, “A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment,” Energy Build., vol. 202, p. 109383, Nov. 2019, doi: 10.1016/j.enbuild.2019.109383.

[22] Z. Wang and R. S. Srinivasan, “A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models,” Renew. Sustain. Energy Rev., vol. 75, pp. 796–808, Aug. 2017, doi: 10.1016/j.rser.2016.10.079.

[23] T. Hong, Z. Wang, X. Luo, and W. Zhang, “State-of-the-art on research and applications of machine learning in the building life cycle,” Energy Build., vol. 212, p. 109831, Apr. 2020, doi: 10.1016/j.enbuild.2020.109831.

[24] E. H. Borgstein, R. Lamberts, and J. L. M. Hensen, “Evaluating energy performance in non-domestic buildings: A review,” Energy Build., vol. 128, pp. 734–755, Sep. 2016, doi: 10.1016/j.enbuild.2016.07.018.

[25] B. Kitchenham, “Procedures for Performing Systematic Reviews,” Keele, UK, Keele Univ., vol. 33, no. 2004, pp. 1–26, 2004.

[26] M. Ahmad, A. Mouraud, Y. Rezgui, and M. Mourshed, “Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption,” Energies, vol. 11, no. 12, p. 3408, Dec. 2018, doi: 10.3390/en11123408.

[27] T. Ahmad, H. Chen, J. Shair, and C. Xu, “Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management,” Int. J. Refrig., vol. 98, pp. 399–409, Feb. 2019, doi: 10.1016/j.ijrefrig.2018.10.017.

[28] T. Ahmad et al., “Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment,” Energy, vol. 158, pp. 17–32, Sep. 2018, doi: 10.1016/j.energy.2018.05.169.

[29] A. Almalaq and J. J. Zhang, “Evolutionary Deep Learning-Based Energy Consumption Prediction for Buildings,” IEEE Access, vol. 7, pp. 1520–1531, 2019, doi: 10.1109/ACCESS.2018.2887023.

[30] T. Cerquitelli, G. Malnati, and D. Apiletti, “Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings,” Energies, vol. 12, no. 15, p. 2933, Jul. 2019, doi: 10.3390/en12152933.

[31] Y. Chen and H. Tan, “Short-term prediction of electric demand in building sector via hybrid support vector regression,” Appl. Energy, vol. 204, pp. 1363–1374, Oct. 2017, doi: 10.1016/j.apenergy.2017.03.070.

[32] Y. Chen, H. Tan, and U. Berardi, “Day-ahead prediction of hourly electric demand in non-stationary operated commercial buildings: A clustering-based hybrid approach,” Energy Build., vol. 148, pp. 228–237, Aug. 2017, doi: 10.1016/j.enbuild.2017.05.003.

[33] B. Al Tarhuni, A. Naji, P. G. Brodrick, K. P. Hallinan, R. J. Brecha, and Z. Yao, “Large scale residential energy efficiency prioritization enabled by machine learning,” Energy Effic., vol. 12, no. 8, pp. 2055–2078, Dec. 2019, doi: 10.1007/s12053-019-09792-0.

[34] M. Al-Rakhami, A. Gumaei, A. Alsanad, A. Alamri, and M. M. Hassan, “An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings,” IEEE Access, vol. 7, pp. 48328–48338, 2019, doi: 10.1109/ACCESS.2019.2909470.

[35] X. Chen and H. Yang, “A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios,” Appl. Energy, vol. 206, pp. 541–557, Nov. 2017, doi: 10.1016/j.apenergy.2017.08.204.

[36] G. Ciulla and A. D’Amico, “Building energy performance forecasting: A multiple linear regression approach,” Appl. Energy, vol. 253, p. 113500, Nov. 2019, doi: 10.1016/j.apenergy.2019.113500.

[37] G. Ciulla, A. D’Amico, V. Lo Brano, and M. Traverso, “Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level,” Energy, vol. 176, pp. 380–391, Jun. 2019, doi: 10.1016/j.energy.2019.03.168.

[38] A. D’Amico, G. Ciulla, M. Traverso, V. Lo Brano, and E. Palumbo, “Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study,” J. Clean. Prod., vol. 239, p. 117993, Dec. 2019, doi: 10.1016/j.jclepro.2019.117993.

[39] Q. Dong, K. Xing, and H. Zhang, “Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions,” Sustainability, vol. 10, no. 2, p. 84, Dec. 2017, doi: 10.3390/su10010084.

[40] 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., vol. 84, p. 105748, Nov. 2019, doi: 10.1016/j.asoc.2019.105748.

[41] P. Geyer and S. Singaravel, “Component-based machine learning for performance prediction in building design,” Appl. Energy, vol. 228, pp. 1439–1453, Oct. 2018, doi: 10.1016/j.apenergy.2018.07.011.

[42] F. Re Cecconi, N. Moretti, and L. C. Tagliabue, “Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings,” Renew. Sustain. Energy Rev., vol. 110, pp. 266–277, Aug. 2019, doi: 10.1016/j.rser.2019.04.073.

[43] D. Tien Bui, H. Moayedi, D. Anastasios, and L. Kok Foong, “Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models,” Appl. Sci., vol. 9, no. 17, p. 3543, Aug. 2019, doi: 10.3390/app9173543.

[44] S. Kumar, S. K. Pal, and R. P. Singh, “Intra ELM variants ensemble based model to predict energy performance in residential buildings,” Sustain. Energy, Grids Networks, vol. 16, pp. 177–187, Dec. 2018, doi: 10.1016/j.segan.2018.07.001.

[45] L. T. Le, H. Nguyen, J. Dou, and J. Zhou, “A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning,” Appl. Sci., vol. 9, no. 13, p. 2630, Jun. 2019, doi: 10.3390/app9132630.

[46] L. T. Le, H. Nguyen, J. Zhou, J. Dou, and H. Moayedi, “Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost,” Appl. Sci., vol. 9, no. 13, p. 2714, Jul. 2019, doi: 10.3390/app9132714.

[47] Z. Li, J. Dai, H. Chen, and B. Lin, “An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage,” Build. Simul., vol. 12, no. 4, pp. 665–681, Aug. 2019, doi: 10.1007/s12273-019-0538-0.

[48] H. Moayedi, D. T. Bui, A. Dounis, Z. Lyu, and L. K. Foong, “Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques,” Appl. Sci., vol. 9, no. 20, p. 4338, Oct. 2019, doi: 10.3390/app9204338.

[49] S. Naji et al., “Estimating building energy consumption using extreme learning machine method,” Energy, vol. 97, pp. 506–516, Feb. 2016, doi: 10.1016/j.energy.2015.11.037.

[50] Navarro-Gonzalez and Villacampa, “An Octahedric Regression Model of Energy Efficiency on Residential Buildings,” Appl. Sci., vol. 9, no. 22, p. 4978, Nov. 2019, doi: 10.3390/app9224978.

[51] N.-T. Ngo, “Early predicting cooling loads for energy-efficient design in office buildings by machine learning,” Energy Build., vol. 182, pp. 264–273, Jan. 2019, doi: 10.1016/j.enbuild.2018.10.004.

[52] M. Nilashi, M. Dalvi-Esfahani, O. Ibrahim, K. Bagherifard, A. Mardani, and N. Zakuan, “A soft computing method for the prediction of energy performance of residential buildings,” Measurement, vol. 109, pp. 268–280, Oct. 2017, doi: 10.1016/j.measurement.2017.05.048.

[53] S. Papadopoulos, E. Azar, W.-L. Woon, and C. E. Kontokosta, “Evaluation of tree-based ensemble learning algorithms for building energy performance estimation,” J. Build. Perform. Simul., vol. 11, no. 3, pp. 322–332, May 2018, doi: 10.1080/19401493.2017.1354919.

[54] S. S. Roy, P. Samui, I. Nagtode, H. Jain, V. Shivaramakrishnan, and B. Mohammadi-ivatloo, “Forecasting heating and cooling loads of buildings: a comparative performance analysis,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 3, pp. 1253–1264, Mar. 2020, doi: 10.1007/s12652-019-01317-y.

[55] A. Sadeghi, R. Younes Sinaki, W. A. Young, and G. R. Weckman, “An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks,” Energies, vol. 13, no. 3, p. 571, Jan. 2020, doi: 10.3390/en13030571.

[56] S. A. R. Sangireddy, A. Bhatia, and V. Garg, “Development of a surrogate model by extracting top characteristic feature vectors for building energy prediction,” J. Build. Eng., vol. 23, pp. 38–52, May 2019, doi: 10.1016/j.jobe.2018.12.018.

[57] S. Sekhar Roy, R. Roy, and V. E. Balas, “Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM,” Renew. Sustain. Energy Rev., vol. 82, pp. 4256–4268, Feb. 2018, doi: 10.1016/j.rser.2017.05.249.

[58] S. Seyedzadeh, F. Pour Rahimian, P. Rastogi, and I. Glesk, “Tuning machine learning models for prediction of building energy loads,” Sustain. Cities Soc., vol. 47, p. 101484, May 2019, doi: 10.1016/j.scs.2019.101484.

[59] S. A. Sharif and A. Hammad, “Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA,” J. Build. Eng., vol. 25, p. 100790, Sep. 2019, doi: 10.1016/j.jobe.2019.100790.

[60] S. Singaravel, J. Suykens, and P. Geyer, “Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction,” Adv. Eng. Informatics, vol. 38, pp. 81–90, Oct. 2018, doi: 10.1016/j.aei.2018.06.004.

[61] W. Tian, S. Yang, J. Zuo, Z. Li, and Y. Liu, “Relationship between built form and energy performance of office buildings in a severe cold Chinese region,” Build. Simul., vol. 10, no. 1, pp. 11–24, Feb. 2017, doi: 10.1007/s12273-016-0314-3.

[62] D.-H. Tran, D.-L. Luong, and J.-S. Chou, “Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings,” Energy, vol. 191, p. 116552, Jan. 2020, doi: 10.1016/j.energy.2019.116552.

[63] F. Ascione, N. Bianco, C. De Stasio, G. M. Mauro, and G. P. Vanoli, “Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach,” Energy, vol. 118, pp. 999–1017, Jan. 2017, doi: 10.1016/j.energy.2016.10.126.

[64] D.-K. Bui, T. N. Nguyen, T. D. Ngo, and H. Nguyen-Xuan, “An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings,” ENERGY, vol. 190, Jan. 2020, doi: 10.1016/j.energy.2019.116370.

[65] G. Zhou, H. Moayedi, M. Bahiraei, and Z. Lyu, “Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings,” J. Clean. Prod., vol. 254, May 2020, doi: 10.1016/j.jclepro.2020.120082.

[66] Q. Dong, K. Xing, and H. Zhang, “Artificial neural network for assessment of energy consumption and cost for cross laminated timber office building in severe cold regions,” Sustain., vol. 10, no. 1, Dec. 2017, doi: 10.3390/su10010084.

[67] A. Sadeghi, R. Y. Sinaki, W. A. Young, and G. R. Weckman, “An intelligent model to predict energy performances of residential buildings based on deep neural networks,” Energies, vol. 13, no. 3, 2020, doi: 10.3390/en13030571.

[68] N. Wang et al., “Ten questions concerning future buildings beyond zero energy and carbon neutrality *,” 2017, doi: 10.1016/j.buildenv.2017.04.006.

[69] 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.

[70] S. Yeom, H. Kim, T. Hong, and M. Lee, “Determining the optimal window size of office buildings considering the workers’ task performance and the building’s energy consumption,” Build. Environ., vol. 177, p. 106872, Jun. 2020, doi: 10.1016/j.buildenv.2020.106872.

[71] L. Badarnah, Y. Nachman Farchi, and U. Knaack, “Solutions from nature for building envelope thermoregulation,” WIT Trans. Ecol. Environ., vol. 138, pp. 251–262, 2010, doi: 10.2495/DN100221.

[72] C. Y. Siu and Z. Liao, “Is building energy simulation based on TMY representative: A comparative simulation study on doe reference buildings in Toronto with typical year and historical year type weather files,” Energy Build., vol. 211, p. 109760, Mar. 2020, doi: 10.1016/j.enbuild.2020.109760.

[73] S. Kumar, S. K. Pal, and R. P. Singh, “Intra ELM variants ensemble based model to predict energy performance in residential buildings,” Sustain. Energy, Grids Networks, vol. 16, pp. 177–187, Dec. 2018, doi: 10.1016/j.segan.2018.07.001.

[74] G. Yun, K. Chun Yoon, and K. Soo Kim, “The influence of shading control strategies on the visual comfort and energy demand of office buildings,” Energy Build., vol. 84, pp. 70–85, 2014, doi: 10.1016/j.enbuild.2014.07.040.

[75] Y. Ding, X. Ma, S. Wei, and W. Chen, “A prediction model coupling occupant lighting and shading behaviors in private offices,” Energy Build., vol. 216, p. 109939, Jun. 2020, doi: 10.1016/j.enbuild.2020.109939.

[76] S. K. Alghoul, H. G. Rijabo, and M. E. Mashena, “Energy consumption in buildings: A correlation for the influence of window to wall ratio and window orientation in Tripoli, Libya,” 2017, doi: 10.1016/j.jobe.2017.04.003.

[77] W. J. Hee et al., “The role of window glazing on daylighting and energy saving in buildings,” Renewable and Sustainable Energy Reviews, vol. 42. Elsevier Ltd, pp. 323–343, 01-Feb-2015, doi: 10.1016/j.rser.2014.09.020.

[78] R. Sullivan, D. K. Arasteh, F. A. Beck, and S. E. Selkowitz, “Energy performance of evacuated glazings in residential buildings,” ASHRAE Trans., vol. 102, no. 2, pp. 220–227, 1996.

[79] I. Andrić, A. Pina, P. Ferrão, J. Fournier, B. Lacarrière, and O. Le Corre, “ScienceDirect ScienceDirect The 15th International Symposium on District Heating and Cooling Assessing the feasibility of using the heat demand-outdoor temperature function for a long-term district heat demand forecast-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling,” Energy Procedia, vol. 147, pp. 207–213, 2018, doi: 10.1016/j.egypro.2018.07.061.

[80] B. Tejedor, M. Casals, M. Macarulla, and A. Giretti, “U-value time series analyses: Evaluating the feasibility of in-situ short-lasting IRT tests for heavy multi-leaf walls,” 2019, doi: 10.1016/j.buildenv.2019.05.001.