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

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A review on buildings energy consumption information

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

DOI 10.1016/j.enbuild.2007.03.007

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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

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“However, these systems account for 40–60% of energy usage in buildings [1] or 15% of the world’s total energy consumption (Rafique 2018).”
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Biotechnological processes for biodiesel production using alternative oils
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“The world energy crisis is a result of population growth and increasing consumption of energy in both developed countries and emerging economies (Pérez-Lombard et al. 2008).”
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A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study
Huang, H. M., Chen, L., & Hu, E. J. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.01.037 · Cited by 182
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“We examined three types of green behavior, each of which has a significant impact on energy consumption (Pérez-Lombard et al. 2008) and/or the environment: (1) recycling behavior, or the process of placing waste materials (e.g., paper, cardboard, aluminum cans, plastic cups/bottles, glass, toner,…”
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“[1,2].”
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“Since statistics concerning detailed energy consumption are usually poor [4,6,23], a theoretical model could provide a more coherent energy analysis.”
Background
A model that connects information technology and hotel performance
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“The heating and cooling energy demand represent more than 40% of the consumption in residential buildings in Europe [27,28].”
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“A major proportion (up to 40%) of the total global energy demand is consumed by urban buildings [1]; of which, up to ~40% is consumed by commercial buildings [2].”
Background
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“However, as the built environment becomes increasingly complex, with multiple uses, larger buildings and higher demand for services such as thermal comfort and data processing [4], the analysis and evaluation of performance in buildings becomes a non-trivial problem.”
Background
Towards a comprehensive life cycle energy analysis framework for residential buildings
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Historical decarbonization of global commercial building operations in the 21st century
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“As a starting point, we focus on the management of lights and HVAC subsystems since they have the highest energy consumption at building level [36].”
Background
Demand side flexibility: Potentials and building performance implications
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“For instance in U.S. residential building, lighting and appliances represent 30% of the electricity consumption [1].”
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Roy, S. S., Roy, R., & Bălaş, V. E. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.249 · Cited by 165
A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction
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“This increased use of energy consumption in the building sector raises concerns about supply issues and global environmental impacts [8].”
Energy usage and technical potential for energy saving measures in the Swedish residential building stock
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“The energy delivered for heat (SH+HW, 80% in the present work) agrees with the available statistics (81% for Sweden in Enerdata, 2010) and with the average of 82% reported for the EU (Pérez-Lombard et al., 2008).”
Result
Optimisation of energy management in commercial buildings with weather forecasting inputs: A review
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A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor
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Climate change impacts on trends and extremes in future heating and cooling demands over Europe
Larsen, M. A. D., Petrović, S. N., Radoszynski, A. M., McKenna, R., & Balyk, O. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110397 · Cited by 160
“Approaches to decarbonise the building stock through energy efﬁciency and renewable energy have therefore been a main focus of research during the past decades [8], and hold signiﬁcant potential for future GHG reductions [10-12].”
Background
Coalescence-induced nanodroplet jumping
Cha, H., Xu, C., Sotelo, J., Chun, J. M., Yokoyama, Y., Enright, R., & Miljković, N. (2016) · Physical Review Fluids · doi:10.1103/physrevfluids.1.064102 · Cited by 110
A comparison of thermal comfort predictive control strategies
Castilla, M., Álvarez, J. D., Berenguel, M., Rodríguez, F., Guzmán, J. L., & García, M. P. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.06.030 · Cited by 158
Deep Sparse Coding for Non–Intrusive Load Monitoring
Singh, S., & Majumdar, A. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2666220 · Cited by 158
“Currently, residential and commercial buildings account for 40% of total energy consumption [1], and studies have estimated that 20% of this consumption could be avoided with improvement in user behavior [2].”
Background
A comprehensive, multi-objective optimization of solar-powered absorption chiller systems for air-conditioning applications
Shirazi, A., Taylor, R. A., Morrison, G. L., & White, S. D. (2016) · Energy Conversion and Management · doi:10.1016/j.enconman.2016.11.039 · Cited by 157
The building performance gap: Are modellers literate?
Imam, S., Coley, D., & Walker, I. O. (2017) · Building Services Engineering Research and Technology · doi:10.1177/0143624416684641 · Cited by 157
Energy consumption in buildings: A correlation for the influence of window to wall ratio and window orientation in Tripoli, Libya
Alghoul, S. K., Rijabo, H. G., & Mashena, M. E. (2017) · Journal of Building Engineering · doi:10.1016/j.jobe.2017.04.003 · Cited by 157
Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction
Liu, Z., Wu, D., Liu, Y., Han, Z., Lun, L., Gao, J., Jin, G., & Cao, G. (2019) · Energy Exploration & Exploitation · doi:10.1177/0144598718822400 · Cited by 163
“In both US and EU, energy demand of residential and commercial buildings accounts for 40% of total energy demand (Huebner et al., 2015; Pérez-Lombard et al., 2008).”
Background
Green Building Rating Systems and the New Framework Level(s): A Critical Review of Sustainability Certification within Europe
Cordero, A. S., Melgar, S. G., & Andújar, J. M. (2019) · Energies · doi:10.3390/en13010066 · Cited by 157
“At the same time, as the construction rate of cities and buildings keeps steadily growing, buildings in developed countries keep on increasing their energy demands to satisfy the inhabitant’s needs [3,4].”
Background
Performance of a coupled cooling system with earth-to-air heat exchanger and solar chimney
Li, H., Yu, Y., Niu, F., Shafik, M. E., & Chen, B. (2013) · Renewable Energy · doi:10.1016/j.renene.2013.08.008 · Cited by 155
“About 50 percent of this energy used in buildings is for heating, cooling, and ventilating buildings [2].”
Background
Refrigerant R32 as lower GWP working fluid in residential air conditioning systems in Europe and the USA
Mota-Babiloni, A., Navarro-Esbrí, J., Makhnatch, P., & Molés, F. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.216 · Cited by 154
Experimental investigation of a super performance dew point air cooler
Xu, P., Ma, X., Zhao, X., & Fancey, K. S. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.06.095 · Cited by 154
“Rapid growth of the world energy consumption in buildings has raised serious concerns over the depletion of energy resources and global climate change [1].”
Background
A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review
Šujanová, P., Rychtáriková, M., Mayor, T. S., & Hyder, A. (2019) · Energies · doi:10.3390/en12081414 · Cited by 167
Techno-economic review of solar heat pump systems for residential heating applications
Poppi, S., Sommerfeldt, N., Bales, C., Madani, H., & Lundqvist, P. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.07.041 · Cited by 153
A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems
Wang, Y., Velswamy, K., & Huang, B. (2017) · Processes · doi:10.3390/pr5030046 · Cited by 157
“Of this amount, the buildings’ Heating, Ventilation and Air Conditioning (HVAC) systems can account for up to 50% of total building energy demand [2,3].”
BackgroundInfluential
Health and wellness in commercial buildings: Systematic review of sustainable building rating systems and alignment with contemporary research
McArthur, J. J., & Powell, C. (2020) · Building and Environment · doi:10.1016/j.buildenv.2019.106635 · Cited by 158
Investigation of heat transfer of a building wall in the presence of phase change material (PCM)
Khan, R. J., Bhuiyan, M. Z. H., & Ahmed, D. H. (2020) · Energy and Built Environment · doi:10.1016/j.enbenv.2020.01.002 · Cited by 153
A long short-term memory artificial neural network to predict daily HVAC consumption in buildings
Sendra-Arranz, R., & Gutiérrez, Á. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.109952 · Cited by 158
Forthcoming perspectives of photoelectrochromic devices: a critical review
Cannavale, A., Cossari, P., Eperon, G. E., Colella, S., Fiorito, F., Gigli, G., Snaith, H. J., & Listorti, A. (2016) · Energy & Environmental Science · doi:10.1039/c6ee01514j · Cited by 153
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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

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