IDEAS home Printed from https://ideas.repec.org/r/eee/rensus/v23y2013icp272-288.html
   My bibliography  Save this item

State of the art in building modelling and energy performances prediction: A review

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Miguel Martínez Comesaña & Sandra Martínez Mariño & Pablo Eguía Oller & Enrique Granada Álvarez & Aitor Erkoreka González, 2020. "A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
  2. Santos, Luis Guilherme Resende & Afshari, Afshin & Norford, Leslie K. & Mao, Jiachen, 2018. "Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect," Applied Energy, Elsevier, vol. 215(C), pages 31-40.
  3. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
  4. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
  5. Marianna Rotilio & Federica Cucchiella & Pierluigi De Berardinis & Vincenzo Stornelli, 2018. "Thermal Transmittance Measurements of the Historical Masonries: Some Case Studies," Energies, MDPI, vol. 11(11), pages 1-18, November.
  6. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
  7. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  8. Xing Shi & Binghui Si & Jiangshan Zhao & Zhichao Tian & Chao Wang & Xing Jin & Xin Zhou, 2019. "Magnitude, Causes, and Solutions of the Performance Gap of Buildings: A Review," Sustainability, MDPI, vol. 11(3), pages 1-21, February.
  9. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
  10. Paulína Šujanová & Monika Rychtáriková & Tiago Sotto Mayor & Affan Hyder, 2019. "A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review," Energies, MDPI, vol. 12(8), pages 1-37, April.
  11. Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
  12. Luca Evangelisti & Claudia Guattari & Paola Gori & Roberto De Lieto Vollaro, 2015. "In Situ Thermal Transmittance Measurements for Investigating Differences between Wall Models and Actual Building Performance," Sustainability, MDPI, vol. 7(8), pages 1-11, August.
  13. Okochi, Godwine Swere & Yao, Ye, 2016. "A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 784-817.
  14. Hillary, Jason & Walsh, Ed & Shah, Amip & Zhou, Rongliang & Walsh, Pat, 2017. "Guidelines for developing efficient thermal conduction and storage models within building energy simulations," Energy, Elsevier, vol. 125(C), pages 211-222.
  15. Mehrdad Rabani & Habtamu Bayera Madessa & Natasa Nord, 2021. "Building Retrofitting through Coupling of Building Energy Simulation-Optimization Tool with CFD and Daylight Programs," Energies, MDPI, vol. 14(8), pages 1-23, April.
  16. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
  17. Ceballos-Fuentealba, Irlanda & Álvarez-Miranda, Eduardo & Torres-Fuchslocher, Carlos & del Campo-Hitschfeld, María Luisa & Díaz-Guerrero, John, 2019. "A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings," Applied Energy, Elsevier, vol. 256(C).
  18. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
  19. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
  20. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  21. Zupančič, Jernej & Filipič, Bogdan & Gams, Matjaž, 2020. "Genetic-programming-based multi-objective optimization of strategies for home energy-management systems," Energy, Elsevier, vol. 203(C).
  22. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  23. Lu, Yanyu & Dong, Jiankai & Liu, Jing, 2020. "Zonal modelling for thermal and energy performance of large space buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  24. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  25. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
  26. Tronchin, Lamberto & Manfren, Massimiliano & Nastasi, Benedetto, 2018. "Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 341-353.
  27. Qihang Zhang & Qinli Deng & Xiaofang Shan & Xin Kang & Zhigang Ren, 2023. "Optimization of the Thermal Environment of Large-Scale Open Space with Subzone-Based Temperature Setting Using BEM and CFD Coupling Simulation," Energies, MDPI, vol. 16(7), pages 1-18, April.
  28. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
  29. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  30. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
  31. Aoun, Nadine & Bavière, Roland & Vallée, Mathieu & Aurousseau, Antoine & Sandou, Guillaume, 2019. "Modelling and flexible predictive control of buildings space-heating demand in district heating systems," Energy, Elsevier, vol. 188(C).
  32. Severinsen, A. & Myrland, Ø., 2022. "Statistical learning to estimate energy savings from retrofitting in the Norwegian food retail market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  33. Gourlis, Georgios & Kovacic, Iva, 2017. "Building Information Modelling for analysis of energy efficient industrial buildings – A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 953-963.
  34. Pedone, Livio & Molaioni, Filippo & Vallati, Andrea & Pampanin, Stefano, 2023. "Energy refurbishment planning of Italian school buildings using data-driven predictive models," Applied Energy, Elsevier, vol. 350(C).
  35. Killian, M. & Zauner, M. & Kozek, M., 2018. "Comprehensive smart home energy management system using mixed-integer quadratic-programming," Applied Energy, Elsevier, vol. 222(C), pages 662-672.
  36. Orosz, Matthew & Altes-Buch, Queralt & Mueller, Amy & Lemort, Vincent, 2018. "Experimental validation of an electrical and thermal energy demand model for rapid assessment of rural health centers in sub-Saharan Africa," Applied Energy, Elsevier, vol. 218(C), pages 382-390.
  37. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
  38. Hamid R. Khosravani & María Del Mar Castilla & Manuel Berenguel & Antonio E. Ruano & Pedro M. Ferreira, 2016. "A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building," Energies, MDPI, vol. 9(1), pages 1-24, January.
  39. Alessandro Franco & Carlo Bartoli & Paolo Conti & Lorenzo Miserocchi & Daniele Testi, 2021. "Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings," Energies, MDPI, vol. 14(10), pages 1-19, May.
  40. Ohlsson, K.E. Anders & Nair, Gireesh & Olofsson, Thomas, 2022. "Uncertainty in model prediction of energy savings in building retrofits: Case of thermal transmittance of windows," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
  41. Anthony Mouraud, 2017. "Innovative time series forecasting: auto regressive moving average vs deep networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 4(3), pages 282-293, March.
  42. Tsai, Sang-Bing, 2018. "Using the DEMATEL model to explore the job satisfaction of research and development professionals in china's photovoltaic cell industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 62-68.
  43. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
  44. Dimitrios K. Panagiotou & Anastasios I. Dounis, 2022. "Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network," Energies, MDPI, vol. 15(17), pages 1-25, September.
  45. Serrano-Guerrero, Xavier & Briceño-León, Marco & Clairand, Jean-Michel & Escrivá-Escrivá, Guillermo, 2021. "A new interval prediction methodology for short-term electric load forecasting based on pattern recognition," Applied Energy, Elsevier, vol. 297(C).
  46. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
  47. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
  48. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
  49. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
  50. Juricic, Sarah & Goffart, Jeanne & Rouchier, Simon & Foucquier, Aurélie & Cellier, Nicolas & Fraisse, Gilles, 2021. "Influence of natural weather variability on the thermal characterisation of a building envelope," Applied Energy, Elsevier, vol. 288(C).
  51. Buonomano, Annamaria & Montanaro, Umberto & Palombo, Adolfo & Santini, Stefania, 2016. "Dynamic building energy performance analysis: A new adaptive control strategy for stringent thermohygrometric indoor air requirements," Applied Energy, Elsevier, vol. 163(C), pages 361-386.
  52. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
  53. Bienvenido-Huertas, David & Moyano, Juan & Marín, David & Fresco-Contreras, Rafael, 2019. "Review of in situ methods for assessing the thermal transmittance of walls," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 356-371.
  54. Naveed Ahmad & Christian Ghiaus & Moomal Qureshi, 2020. "Error Analysis of QUB Method in Non-Ideal Conditions during the Experiment," Energies, MDPI, vol. 13(13), pages 1-17, July.
  55. Kangji Li & Lei Pan & Wenping Xue & Hui Jiang & Hanping Mao, 2017. "Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study," Energies, MDPI, vol. 10(2), pages 1-23, February.
  56. Ohlsson, K.E. Anders & Olofsson, Thomas, 2021. "Benchmarking the practice of validation and uncertainty analysis of building energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
  57. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  58. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2020. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls," Energies, MDPI, vol. 13(12), pages 1-18, June.
  59. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
  60. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
  61. Heegang Kim & Myoungsouk Yeo, 2020. "Thermal Bridge Modeling and a Dynamic Analysis Method Using the Analogy of a Steady-State Thermal Bridge Analysis and System Identification Process for Building Energy Simulation: Methodology and Vali," Energies, MDPI, vol. 13(17), pages 1-22, August.
  62. Mattia De Rosa & Marcus Brennenstuhl & Carlos Andrade Cabrera & Ursula Eicker & Donal P. Finn, 2019. "An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation," Energies, MDPI, vol. 12(12), pages 1-20, June.
  63. Seunghyeon Wang & Hyeonyong Hae & Juhyung Kim, 2018. "Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR," Energies, MDPI, vol. 11(2), pages 1-14, February.
  64. Kotarela, Faidra & Kyritsis, Anastasios & Agathokleous, Rafaela & Papanikolaou, Nick, 2023. "On the exploitation of dynamic simulations for the design of buildings energy systems," Energy, Elsevier, vol. 271(C).
  65. Altieri, Domenico & Patel, Martin K. & Lazarus, Joël & Branca, Giovanni, 2023. "Numerical analysis of low-cost optimization measures for improving energy efficiency in residential buildings," Energy, Elsevier, vol. 273(C).
  66. Mauricio Nath Lopes & Roberto Lamberts, 2018. "Development of a Metamodel to Predict Cooling Energy Consumption of HVAC Systems in Office Buildings in Different Climates," Sustainability, MDPI, vol. 10(12), pages 1-25, December.
  67. Roberts, Mike B. & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2019. "Characterisation of Australian apartment electricity demand and its implications for low-carbon cities," Energy, Elsevier, vol. 180(C), pages 242-257.
  68. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
  69. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
  70. Yanxia Li & Chao Wang & Sijie Zhu & Junyan Yang & Shen Wei & Xinkai Zhang & Xing Shi, 2020. "A Comparison of Various Bottom-Up Urban Energy Simulation Methods Using a Case Study in Hangzhou, China," Energies, MDPI, vol. 13(18), pages 1-23, September.
  71. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
  72. Luca Evangelisti & Claudia Guattari & Paola Gori, 2015. "Energy Retrofit Strategies for Residential Building Envelopes: An Italian Case Study of an Early-50s Building," Sustainability, MDPI, vol. 7(8), pages 1-16, August.
  73. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
  74. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
  75. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
  76. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
  77. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
  78. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
  79. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
  80. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
  81. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
  82. Petri Hietaharju & Mika Ruusunen & Kauko Leiviskä, 2018. "A Dynamic Model for Indoor Temperature Prediction in Buildings," Energies, MDPI, vol. 11(6), pages 1-20, June.
  83. Fan, Xinying, 2022. "A method for the generation of typical meteorological year data using ensemble empirical mode decomposition for different climates of China and performance comparison analysis," Energy, Elsevier, vol. 240(C).
  84. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
  85. Rahman, Aowabin & Smith, Amanda D., 2018. "Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms," Applied Energy, Elsevier, vol. 228(C), pages 108-121.
  86. Naveros, I. & Ghiaus, C., 2015. "Order selection of thermal models by frequency analysis of measurements for building energy efficiency estimation," Applied Energy, Elsevier, vol. 139(C), pages 230-244.
  87. Touretzky, Cara R. & Patil, Rakesh, 2015. "Building-level power demand forecasting framework using building specific inputs: Development and applications," Applied Energy, Elsevier, vol. 147(C), pages 466-477.
  88. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2020. "A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models," Applied Energy, Elsevier, vol. 275(C).
  89. Sun, Xiaoqin & Medina, Mario A. & Lee, Kyoung Ok & Jin, Xing, 2018. "Laboratory assessment of residential building walls containing pipe-encapsulated phase change materials for thermal management," Energy, Elsevier, vol. 163(C), pages 383-391.
  90. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
  91. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
  92. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2021. "Feature assessment frameworks to evaluate reduced-order grey-box building energy models," Applied Energy, Elsevier, vol. 298(C).
  93. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
  94. Sergio Ortega Alba & Mario Manana, 2017. "Characterization and Analysis of Energy Demand Patterns in Airports," Energies, MDPI, vol. 10(1), pages 1-35, January.
  95. 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.
  96. Soutullo, S. & Giancola, E. & Heras, M.R., 2018. "Dynamic energy assessment to analyze different refurbishment strategies of existing dwellings placed in Madrid," Energy, Elsevier, vol. 152(C), pages 1011-1023.
  97. Daeho Kim & Jimin Kim & Choongwan Koo & Taehoon Hong, 2014. "An Economic and Environmental Assessment Model for Selecting the Optimal Implementation Strategy of Fuel Cell Systems—A Focus on Building Energy Policy," Energies, MDPI, vol. 7(8), pages 1-22, August.
  98. Lu, Yakai & Tian, Zhe & Zhang, Qiang & Zhou, Ruoyu & Chu, Chengshan, 2021. "Data augmentation strategy for short-term heating load prediction model of residential building," Energy, Elsevier, vol. 235(C).
  99. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
  100. Clara Vite & Renata Morbiducci, 2021. "Optimizing the Sustainable Aspects of the Design Process through Building Information Modeling," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
  101. Tsai, Sang-Bing & Xue, Youzhi & Zhang, Jianyu & Chen, Quan & Liu, Yubin & Zhou, Jie & Dong, Weiwei, 2017. "Models for forecasting growth trends in renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1169-1178.
  102. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
  103. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
  104. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
  105. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
  106. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
  107. Xiao, Tong & Xu, Peng & He, Ruikai & Sha, Huajing, 2022. "Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition," Applied Energy, Elsevier, vol. 305(C).
  108. Hong, Taehoon & Koo, Choongwan & Kim, Daeho & Lee, Minhyun & Kim, Jimin, 2015. "An estimation methodology for the dynamic operational rating of a new residential building using the advanced case-based reasoning and stochastic approaches," Applied Energy, Elsevier, vol. 150(C), pages 308-322.
  109. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2021. "Energy Flexibility as Additional Energy Source in Multi-Energy Systems with District Cooling," Energies, MDPI, vol. 14(2), pages 1-30, January.
  110. Shaoxiong Li & Le Liu & Changhai Peng, 2020. "A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges," Sustainability, MDPI, vol. 12(4), pages 1-36, February.
  111. Azar, Elie & Al Ansari, Hamad, 2017. "Framework to investigate energy conservation motivation and actions of building occupants: The case of a green campus in Abu Dhabi, UAE," Applied Energy, Elsevier, vol. 190(C), pages 563-573.
  112. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
  113. Rackes, Adams & Melo, Ana Paula & Lamberts, Roberto, 2016. "Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates," Applied Energy, Elsevier, vol. 174(C), pages 256-274.
  114. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
  115. Buonomano, Annamaria & Palombo, Adolfo, 2014. "Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies," Applied Energy, Elsevier, vol. 113(C), pages 788-807.
  116. De Boeck, L. & Verbeke, S. & Audenaert, A. & De Mesmaeker, L., 2015. "Improving the energy performance of residential buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 960-975.
  117. Perera, D.W.U. & Winkler, D. & Skeie, N.-O., 2016. "Multi-floor building heating models in MATLAB and Modelica environments," Applied Energy, Elsevier, vol. 171(C), pages 46-57.
  118. Evola, G. & Marletta, L., 2015. "The Solar Response Factor to calculate the cooling load induced by solar gains," Applied Energy, Elsevier, vol. 160(C), pages 431-441.
  119. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
  120. Salata, Ferdinando & Ciancio, Virgilio & Dell'Olmo, Jacopo & Golasi, Iacopo & Palusci, Olga & Coppi, Massimo, 2020. "Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms," Applied Energy, Elsevier, vol. 260(C).
  121. Schreiber, Thomas & Netsch, Christoph & Eschweiler, Sören & Wang, Tianyuan & Storek, Thomas & Baranski, Marc & Müller, Dirk, 2021. "Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system," Energy, Elsevier, vol. 230(C).
  122. Wiethe, Christian & Wenninger, Simon, 2023. "The influence of building energy performance prediction accuracy on retrofit rates," Energy Policy, Elsevier, vol. 177(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.