Cited by 6,649Building Energy and Comfort OptimizationBuilding energy efficiency and sustainabilityEnergy Efficiency and Management

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A review on buildings energy consumption information

Open access

Pérez-Lombard, Luis, Ortiz, José, Pout, Christine·2008·Energy and Buildings

DOI 10.1016/j.enbuild.2007.03.007

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.enbuild.2007.03.007

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Rating the energy performance of buildings
Olofsson, T., Meier, A., & Lamberts, R. (2004) · University of North Texas Digital Library (University of North Texas) · Cited by 232

Show all 1 references

Sorted by citations

Items 501–600 of 1,007

Intelligent building, definitions, factors and evaluation criteria of selection
Omar, O. (2018) · Alexandria Engineering Journal · doi:10.1016/j.aej.2018.07.004 · Cited by 107
Data-driven modeling of building thermal dynamics: Methodology and state of the art
Wang, Z., & Chen, Y. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109405 · Cited by 107
Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm
Aslam, M. S., Ghazal, T. M., Fatima, A., Said, R. A., Abbas, S., Khan, M. A., Siddiqui, S. Y., & Ahmad, M. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2021.017920 · Cited by 107
Exploring the Role of Adsorption and Surface State on the Hydrophobicity of Rare Earth Oxides
Lundy, R., Byrne, C., Bogan, J., Nolan, K., Collins, M. N., Dalton, E., & Enright, R. (2017) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.7b01515 · Cited by 109
Solutions to reduce energy consumption in the management of large buildings
Colmenar‐Santos, A., de Lober, L. N. T., Borge-Díez, D., & Castro-Gil, M. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.10.004 · Cited by 107
Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption
Qian, F., Gao, W., Yang, Y., & Yu, D. (2019) · Energy · doi:10.1016/j.energy.2019.116724 · Cited by 106
Experimental investigation on the thermal performance of double-layer PCM radiant floor system containing two types of inorganic composite PCMs
Sun, W., Zhang, Y., Ling, Z., Fang, X., & Zhang, Z. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.109806 · Cited by 106
Recent advances and development in optimal design and control of ground source heat pump systems
Ma, Z., Xia, L., Gong, X., Kokogiannakis, G., Wang, S., & Zhou, X. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110001 · Cited by 106
Short-term effects of natural view and daylight from windows on thermal perception, health, and energy-saving potential
Jiang, Y., Li, N., Yongga, A., & Yan, W. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108575 · Cited by 106
Compliance with building energy regulations for new-build dwellings
Pan, W., & Garmston, H. (2012) · Energy · doi:10.1016/j.energy.2012.06.048 · Cited by 105
Embodied energy on refurbishment vs. demolition: A southern Europe case study
Gaspar, P. L., & Santos, A. L. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.11.040 · Cited by 106
Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method
Kim, W., Jeon, Y., & Kim, Y. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.10.153 · Cited by 105
RETRACTED: A review disaggregation method in Non-intrusive Appliance Load Monitoring
Esa, N. F. A., Abdullah, M. P., & Hassan, M. Y. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.07.009 · Cited by 105
Retracted
Energy saving evaluation of passive systems for residential buildings in hot and dry regions
Goudarzi, H., & Mostafaeipour, A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.10.002 · Cited by 105
Economic evaluation of building energy retrofits: A fuzzy based approach
Ruparathna, R., Hewage, K., & Sadiq, R. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.01.031 · Cited by 105
Electric load shape benchmarking for small- and medium-sized commercial buildings
Luo, X., Hong, T., Chen, Y., & Piette, M. A. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.07.108 · Cited by 105
Experimental evaluation of a solid desiccant system integrated with cross flow Maisotsenko cycle evaporative cooler
Shahzad, M. K., Chaudhary, G. Q., Ali, M., Sheikh, N. A., Khalil, M. S., & Rashid, T. U. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.09.105 · Cited by 105
Investigating the potential for energy flexibility in an office building with a vertical BIPV and a PV roof system
Aelenei, D., Lopes, R. A., Aelenei, L., & Gonçalves, H. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.07.140 · Cited by 105
Indoor thermal environment optimal control for thermal comfort and energy saving based on online monitoring of thermal sensation
Li, W., Zhang, J., & Zhao, T. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.05.050 · Cited by 105
Improving energy efficiency of HVAC systems in buildings: a review of best practices
Simpeh, E. K., Pillay, J.-P. G., Ndihokubwayo, R., & Nalumu, D. J. (2021) · International Journal of Building Pathology and Adaptation · doi:10.1108/ijbpa-02-2021-0019 · Cited by 122
Energy and carbon emission payback analysis for energy-efficient retrofitting in buildings—Overhang shading option
Huang, Y. J., Niu, J., & Chung, T. M. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.10.027 · Cited by 105
A Hedera green façade – Energy performance and saving under different maritime-temperate, winter weather conditions
Cameron, R. W. F., Taylor, J., & Emmett, M. R. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.04.011 · Cited by 105
Comparison between evaporative cooling and a heat pipe assisted thermal loop for a commercial wind tower in hot and dry climatic conditions
Calautit, J. K., Chaudhry, H. N., Hughes, B. R., & Ghani, S. A. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.07.034 · Cited by 104
A fuzzy multi-criteria decision making approach to assess building energy performance
Kabak, M., Köse, E., Kırılmaz, O., & Burmaoğlu, S. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2013.12.059 · Cited by 104
Fast and Accurate Classification of Time Series Data Using Extended ELM: Application in Fault Diagnosis of Air Handling Units
Yan, K., Ji, Z., Lu, H., Huang, J., Shen, W., & Xue, Y. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2691774 · Cited by 104
“tion [2], [3].”
Thermal conductivity measurement of thermal insulating mortars with EPS and silica aerogel by steady-state and transient methods
Gomes, M. G., Flores‐Colen, I., da Silva, F., & Pedroso, M. F. (2018) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2018.03.162 · Cited by 109
Predictive models for daylight performance of general floorplans based on CNN and GAN: A proof-of-concept study
He, Q., Li, Z., Gao, W., Chen, H., Wu, X., Cheng, X., & Lin, B. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108346 · Cited by 107
Grid-Connected Microgrids: Demand Management via Distributed Control and Human-in-the-Loop Optimization
Korkas, C. D., Baldi, S., & Kosmatopoulos, E. B. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813185-5.00025-5 · Cited by 103
Life Cycle Environmental and Cost Performance of Prefabricated Buildings
Wang, H., Zhang, Y., Gao, W., & Kuroki, S. (2020) · Sustainability · doi:10.3390/su12072609 · Cited by 103
Depletion of Lubricant from Nanostructured Oil-Infused Surfaces by Pendant Condensate Droplets.
Adera, S., Alvarenga, J., Shneidman, A. V., Zhang, C. T., Davitt, A., & Aizenberg, J. (2020) · ACS Nano · doi:10.1021/acsnano.9b10184 · Cited by 96
Optimizing building energy consumption in office buildings: A review of building automation and control systems and factors influencing energy savings
Vandenbogaerde, L., Verbeke, S., & Audenaert, A. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107233 · Cited by 103
User‐centric smart buildings for energy sustainable smart cities
Moreno, M. V., Zamora, M. Á., & Skármeta, A. (2013) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.2771 · Cited by 103
Modeling and optimization of a chiller plant
Wei, X., Xu, G., & Kusiak, A. (2014) · Energy · doi:10.1016/j.energy.2014.06.102 · Cited by 103
Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms
Zeng, Y., Zhang, Z., & Kusiak, A. (2015) · Energy · doi:10.1016/j.energy.2015.04.045 · Cited by 103
Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method
Kusiak, A., Xu, G., & Zhang, Z. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.05.053 · Cited by 106
Demand controlled ventilation (DCV) in school and office buildings: Lessons learnt from case studies
Merema, B., Delwati, M., Sourbron, M., & Breesch, H. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.04.065 · Cited by 102
Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings
Yang, B., Cheng, X., Dai, D., Olofsson, T., Li, H., & Meier, A. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106284 · Cited by 103
“About 21% of global energy consumption occurs in building section, and roughly half of which is consumed by heating, ventilation and air conditioning (HVAC) systems in order to provide thermal comfort to their occupants [1, 2].”
Background
Thermal energy regulation with 3D printed polymer-phase change material composites
Wei, P., Cipriani, C. E., & Pentzer, E. (2021) · Matter · doi:10.1016/j.matt.2021.03.019 · Cited by 102
Radiative cooling for buildings: A review of techno-enviro-economics and life-cycle assessment methods
Pirvaram, A., Talebzadeh, N., Leung, S. N., & O’Brien, P. G. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112415 · Cited by 102
Identifying key variables and interactions in statistical models of building energy consumption using regularization
Hsu, D. (2015) · Energy · doi:10.1016/j.energy.2015.02.008 · Cited by 99
“Approximately 40% of all primary energy worldwide is consumed in and by buildings [3].”
Background
How much information disclosure of building energy performance is necessary?
Hsu, D. (2013) · Energy Policy · doi:10.1016/j.enpol.2013.08.094 · Cited by 100
“Estimates of the portion of total energy used by buildings range from 20% to 40% worldwide, and is expected to grow rapidly in future years (Pérez-Lombard et al., 2008).”
Background
Control-Oriented Thermal Modeling of Multizone Buildings: Methods and Issues: Intelligent Control of a Building System
Atam, E., & Helsen, L. (2016) · IEEE Control Systems · doi:10.1109/mcs.2016.2535913 · Cited by 101
“[2] L.”
Supramolecular strategy for smart windows
Wang, S., Gao, W. M., Hu, X., Shen, Y., & Wang, L. (2019) · Chemical Communications · doi:10.1039/c9cc00273a · Cited by 101
Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions
Ghahramani, A., Galicia, P. J., Lehrer, D., Varghese, Z., Wang, Z., & Pandit, Y. (2020) · Frontiers in Built Environment · doi:10.3389/fbuil.2020.00049 · Cited by 101
Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO
Kardani, N., Bardhan, A., Kim, D., Samui, P., & Zhou, A. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.102105 · Cited by 101
A solar-driven 5th generation district heating and cooling network with ground-source heat pumps: a thermo-economic analysis
Calise, F., Cappiello, F. L., d’Accadia, M. D., Petrakopoulou, F., & Vicidomini, M. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103438 · Cited by 100
Intelligent Management Systems for Energy Efficiency in Buildings
Paola, A. D., Ortolani, M., Re, G. L., Anastasi, G., & Das, S. K. (2014) · ACM Computing Surveys · doi:10.1145/2611779 · Cited by 106
“Residential appliances consume about 30% of the total electricity consumption and produce 12% of all energy-related CO2 emissions; for instance, about 54% of the energy consumption in US residential buildings is due to HVAC systems, and about 6% to artificial lighting, while in commercial buildings HVAC and artificial…”
Background
Hybrid ground coupled heat exchanger systems for space heating/cooling applications: A review
Soni, S. K., Pandey, M., & Bartaria, V. N. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.01.125 · Cited by 100
The use of 3D GIS to analyse the influence of urban context on buildings’ solar energy potential
Machete, R., Falcão, A. P., Gomes, M. G., & Rodrigues, A. M. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.07.064 · Cited by 100
A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states
Yun, W.-S., Hong, W., & Seo, H. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.102111 · Cited by 100
A dynamic anomaly detection method of building energy consumption based on data mining technology
Lei, L., Wu, B., Fang, X., Chen, L., Wu, H., & Liu, W. (2022) · Energy · doi:10.1016/j.energy.2022.125575 · Cited by 100
Fault Detection Analysis of Building Energy Consumption Using Data Mining Techniques
Khan, I., Capozzoli, A., Corgnati, S. P., & Cerquitelli, T. (2013) · Energy Procedia · doi:10.1016/j.egypro.2013.11.057 · Cited by 100
“Increasing demand for building services and high thermal comfort levels, together with the amount of time spent indoors, will increase the energy demand in future [1].”
Background
Embodied energy consumption of building construction engineering: Case study in E-town, Beijing
Han, M., Chen, G. Q., Shao, L., Li, J., Alsaedi, A., Ahmad, B., Guo, S., Jiang, M. M., & Ji, X. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.04.006 · Cited by 99
Synthesis of Highly Crystalline NH2-MIL-125 (Ti) with S-Shaped Water Isotherms for Adsorption Heat Transformation
Sohail, M., Yun, Y., Lee, E., Kim, S. K., Cho, K., Kim, J., Kim, T. W., Moon, J.-H., & Kim, H. (2017) · Crystal Growth & Design · doi:10.1021/acs.cgd.6b01597 · Cited by 99
Robust thin film composite PDMS/PAN hollow fiber membranes for water vapor removal from humid air and gases
Liang, C. Z., & Chung, T. (2018) · Separation and Purification Technology · doi:10.1016/j.seppur.2018.03.005 · Cited by 99
Metal-organic framework MIL-100(Fe) as a novel moisture buffer material for energy-efficient indoor humidity control
Feng, X., Qin, M., Cui, S., & Rode, C. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.09.027 · Cited by 99
“Introduction 27 Buildings account for about 40% of the world's total energy consumption [1, 2], and more than 28 50% of the primary energy consumed in buildings is for the heating and air-conditioning system 29 [3].”
Background
Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads
Kazmi, H., Suykens, J. A. K., Bálint, A., & Driesen, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.140 · Cited by 99
Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants' heating behavior
Katić, K., Li, R., & Zeiler, W. (2020) · Applied Ergonomics · doi:10.1016/j.apergo.2020.103078 · Cited by 102
A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction
Jallal, M. A., González-Vidal, A., Skármeta, A., Chabaa, S., & Zeroual, A. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114977 · Cited by 99
Thermal Management by Engineering the Alignment of Nanocellulose
Smalyukh, I. I. (2020) · Advanced Materials · doi:10.1002/adma.202001228 · Cited by 99
Solar Energy in the United States: Development, Challenges and Future Prospects
Tabassum, S., Rahman, T., Islam, A. U., Rahman, S., Dipta, D. R., Roy, S., Mohammad, N., Nawar, N., & Hossain, E. (2021) · Energies · doi:10.3390/en14238142 · Cited by 99
“Space Heating and Cooling The energy consumed by the heating, ventilation, and air conditioning (HVAC) system of a building constitutes more than 50% of the building’s total energy consumption in the U.S., and as high as 70% in Australia [43].”
BackgroundInfluential
Towards a universal energy efficiency index for buildings
González, A. B. R., Díaz, J. J. V., Caamaño, A. J., & Wilby, M. R. (2010) · Energy and Buildings · doi:10.1016/j.enbuild.2010.12.023 · Cited by 99
Impact of phase change materials types and positioning on hot water tank thermal performance: Using measured water demand profile
Nkwetta, D. N., Vouillamoz, P.-E., Haghighat, F., Mankibi, M. E., Moreau, A., & Daoud, A. (2014) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2014.03.051 · Cited by 99
Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm
Kusiak, A., Xu, G., & Tang, F. (2011) · Energy · doi:10.1016/j.energy.2011.08.024 · Cited by 99
“Heating, ventilating, and air conditioning (HVAC) systems, account for over 50% of the energy consumed by buildings [1].”
Scaling-up low-carbon urban initiatives: Towards a better understanding
van Doren, D., Driessen, P., Runhaar, H. A. C., & Giezen, M. (2016) · Urban Studies · doi:10.1177/0042098016640456 · Cited by 98
Overview of solar technologies for electricity, heating and cooling production
Settino, J., Sant, T., Micallef, C., Farrugia, M., Staines, C. S., Licari, J., & Micallef, A. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.112 · Cited by 98
Prospective life cycle assessment of third-generation photovoltaics at the pre-industrial scale: A long-term scenario approach
Parisi, M. L., Maranghi, S., Vesce, L., Sinicropi, A., Carlo, A. D., & Basosi, R. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109703 · Cited by 105
A comprehensive review: Fault detection, diagnostics, prognostics, and fault modeling in HVAC systems
Singh, V., Mathur, J., & Bhatia, A. (2022) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2022.08.017 · Cited by 98
A review of physics-informed machine learning for building energy modeling
Ma, Z., Jiang, G. Y., Hu, Y., & Chen, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125169 · Cited by 111
Recycling concepts and the index of recyclability for building materials
Vefago, L. H. M., & Díaz-Grande, J. A. (2013) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2012.12.015 · Cited by 98
Energy Consumption Prediction Using Machine Learning; A ReviewPreprint
Mosavi, A. H., & Bahmani, A. (2019) · Preprints.org · doi:10.20944/preprints201903.0131.v1 · Cited by 107
“Also, It is shown that energy consumption throughout the world is rapidly increasing [2].”
Background
Improvement of the thermal inertia of building materials incorporating PCM. Evaluation in the macroscale
Barreneche, C., Navarro, M. E., Fernández, A. I., & Cabeza, L. F. (2013) · Applied Energy · doi:10.1016/j.apenergy.2012.12.055 · Cited by 97
Stochastic Analysis and Forecasts of the Patterns of Speed, Acceleration, and Levels of Material Stock Accumulation in Society
Fishman, T., Schandl, H., & Tanikawa, H. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b05790 · Cited by 97
A sensor fault detection strategy for air handling units using cluster analysis
Yan, R., Ma, Z., Kokogiannakis, G., & Zhao, Y. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.06.005 · Cited by 97
Development of thermal energy storage cementitious composites (TESC) containing a novel paraffin/hydrophobic expanded perlite composite phase change material
Ramakrishnan, S., Wang, X., Sanjayan, J., Petinakis, E., & Wilson, J. L. (2017) · Solar Energy · doi:10.1016/j.solener.2017.09.064 · Cited by 97
Advanced Recycled Polyethylene Terephthalate Aerogels from Plastic Waste for Acoustic and Thermal Insulation Applications
Koh, H. W., Le, D. K., Ng, G. N., Zhang, X., Phan-Thien, N., Kureemun, U., & Duong, H. M. (2018) · Gels · doi:10.3390/gels4020043 · Cited by 97
“Therefore, the use of high performance superinsulation materials, which are superior to conventional insulation materials can ensure both energy and cost savings [9].”
Background
Utilization of bio-chars from sugarcane bagasse pyrolysis in cement-based composites
Rodier, L., Bilba, K., Potiron, C. O., & Arsène, M.-A. (2019) · Industrial Crops and Products · doi:10.1016/j.indcrop.2019.111731 · Cited by 97
Unweaving the human response in daylighting design
Andersen, M. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.03.014 · Cited by 99
Building neighborhood emerging properties and their impacts on multi-scale modeling of building energy and airflows
Srebric, J., Heidarinejad, M., & Liu, J. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.02.031 · Cited by 96
Applications of machine learning methods to identifying and predicting building retrofit opportunities
Marasco, D. E., & Kontokosta, C. E. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.06.092 · Cited by 96
Taxonomy for BIM and Asset Management Semantic Interoperability
Farghaly, K., Abanda, F. H., Vidalakis, C., & Wood, G. (2018) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000610 · Cited by 96
Development of building energy saving advisory: A data mining approach
Ashouri, M., Haghighat, F., Fung, B. C. M., Lazrak, A., & Yoshino, H. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.04.052 · Cited by 96
“Buildings’ impacts on global energy consumption have been increasing steadily, reaching up to 20-40% in developed countries [1].”
Background
A review of heating, ventilation and air conditioning technologies and innovations used in solar-powered net zero energy Solar Decathlon houses
Ma, Z., Ren, H., & Lin, W. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118158 · Cited by 96
A method for evaluating both shading and power generation effects of rooftop solar PV panels for different climate zones of China
Wang, D., Qi, T., Liu, Y., Wang, Y., Fan, J., Wang, Y., & Du, H. (2020) · Solar Energy · doi:10.1016/j.solener.2020.05.009 · Cited by 96
Systems accounting for energy consumption and carbon emission by building
Shao, L., Chen, G. Q., Chen, Z. M., Guo, S., Han, M., Zhang, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2013) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2013.10.003 · Cited by 96
The relationship between house size and life cycle energy demand: Implications for energy efficiency regulations for buildings
Stephan, A., & Crawford, R. H. (2016) · Energy · doi:10.1016/j.energy.2016.10.038 · Cited by 102
A direct load control strategy of centralized air-conditioning systems for building fast demand response to urgent requests of smart grids
Tang, R., Wang, S., & Yan, C. (2017) · Automation in Construction · doi:10.1016/j.autcon.2017.12.012 · Cited by 95
Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods
Song, Y., Mao, F., & Liu, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2937320 · Cited by 99
“In developed countries, HVAC systems typically consume 50%of building energy use [13].”
Background
Advances in the research of building energy saving
Li, C. Z., Zhang, L., Liang, X. Z., Xiao, B., Tam, V. W. Y., Lai, X., & Chen, Z. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111556 · Cited by 97
Building the future: Smart concrete as a key element in next-generation construction
Tian, Q., Zhou, J. P., Hou, J., Zhou, Z., Liang, Z., Sun, M., Hu, J., & Huang, J. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.136364 · Cited by 95
Efficient building energy management using distributed model predictive control
Scherer, H. F., Pasamontes, M., Guzmán, J. L., Álvarez, J. D., Camponogara, E., & Normey-Rico, J. E. (2013) · Journal of Process Control · doi:10.1016/j.jprocont.2013.09.024 · Cited by 95
Development and validation of a new variable refrigerant flow system model in EnergyPlus
Hong, T., Sun, K., Zhang, R., Hinokuma, R., Kasahara, S., & Yura, Y. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.09.023 · Cited by 95
An experimental and theoretical investigation of the extent of bypass air within data centres employing aisle containment, and its impact on power consumption
Tatchell-Evans, M., Kapur, N., Summers, J. L., Thompson, H. M., & Oldham, D. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.03.076 · Cited by 94
Optimized design of low-rise commercial buildings under various climates – Energy performance and passive cooling strategies
Lapisa, R., Bozonnet, E., Salagnac, P., & Abadie, M. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.01.029 · Cited by 94
Uncertainties in whole-building life cycle assessment: A systematic review
Feng, H., Zhao, J., Zhang, H., Zhu, S., Li, D., & Thurairajah, N. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.104191 · Cited by 101
An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis
Wen, S., Zhang, W., Sun, Y., Li, Z., Huang, B., Bian, S., Zhao, L., & Wang, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120862 · Cited by 94
Identifying the Most Efficient Natural Fibre for Common Commercial Building Insulation Materials with an Integrated PSI, MEREC, LOPCOW and MCRAT Model
Ulutaş, A., Balo, F., & Topal, A. (2023) · Polymers · doi:10.3390/polym15061500 · Cited by 108
“However, with increasing concerns about the scarcity of these sources and climate change, there is rising pressure on structure companies to decrease their environmental effect [2,3].”
Background
Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability
Xiao, F., & Wang, S. (2008) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2008.03.006 · Cited by 94
“Most of energy use in buildings is for the provision of heating, ventilation and air conditioning (HVAC), which takes about 50% of building energy consumption on an average [2].”
Background
Operational strategy and marginal costs in simple trigeneration systems
Lozano, M. A., Carvalho, M., & Serra, L. M. (2009) · Energy · doi:10.1016/j.energy.2009.08.015 · Cited by 93
Real-time building occupancy sensing using neural-network based sensor network
Ekwevugbe, T., Brown, N., Pakka, V. H., & Fan, D. (2013) · IEEE International Conference on Digital Ecosystems and Technologies · doi:10.1109/dest.2013.6611339 · Cited by 93
“Approximately about 40% of the world’s energy is consumed by buildings [1], of which roughly about half of this energy is consumed by heating, ventilation, and air conditioning (HVAC) systems [2].”
Background

Authors: Pérez-Lombard, L., Ortiz, J., & Pout, C.
Affiliations: Building Research Establishment (GB)
Venue: Energy and Buildings
Publisher: Elsevier BV
Published: 2008
Volume: 40
Issue: 3
Pages: 394-398
ISSN: 0378-7788 (ISSN-L)
DOI: 10.1016/j.enbuild.2007.03.007
OpenAlex: W2163121678
Cited by: 6,649
References: 1
Citations by year: 2026: 368 · 2025: 445 · 2024: 465 · 2023: 485 · 2022: 475 · 2021: 566 · 2020: 553 · 2019: 545 · 2018: 494 · 2017: 520 · 2016: 434 · 2015: 398 · 2014: 340 · 2013: 234 · 2012: 151
FWCI: 81.62
Type: journal article
Language: en
Open access: green
idus.us.es
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Energy and Buildings (publishedVersion)
LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
Deposito de Investigacion Universidad de Sevilla (University of Seville) (submittedVersion, cc-by-nc-nd)
Topics: Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
Keywords: building energy consumption, HVAC systems, energy efficiency, commercial buildings, office buildings, building types, end-use energy, energy policy, residential buildings, cross-national comparison

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A review on buildings energy consumption information

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks