Smart energy systems for sustainable smart cities: Current developments, trends and future directions
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Due to inherent variability in energy demand and supply, there is a need for uncertainty estimation, e.g., using Markov chain Monte Carlo methods or Gaussian processes.
“Due to inherent variability in energy demand and supply, there is a need for uncertainty estimation, e.g., using Markov chain Monte Carlo methods or Gaussian processes [602].”
From Tackling climate change with machine learning (2023) · cites this paper as [602]
When smart city projects are properly integrated into urban planning, they can make cities more sustainable and foster low-carbon lifestyles.
“When smart city projects are properly integrated into urban planning, they can make cities more sustainable and foster low-carbon lifestyles (see [563,602,853] for extensive reviews on this topic).”
From Tackling climate change with machine learning (2023) · cites this paper as [602]
ML and AI can help coordinate district heating and cooling networks, solar power generation, and charging stations for EVs and bikes, and can improve public lighting systems by regulating light intensity based on historical patterns of foot traffic.
“ML and AI can help, for example, to coordinate district heating and cooling networks, solar power generation, and charging stations for EVs and bikes [602], and can improve public lighting systems by regulating light intensity based on historical patterns of foot traffic [169].”
From Tackling climate change with machine learning (2023) · cites this paper as [602]
Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
(2012) · Cited by 9,062 · see citing works
Internet of Things for Smart Cities
Zanella, A., Bui, N., Castellani, A. P., Vangelista, L., & Zorzi, M. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2306328 · Cited by 6,385 · see citing works
Gaussian Processes in Machine Learning
Rasmussen, C. E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28650-9_4 · Cited by 5,727 · see citing works
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,466 · see citing works
Predictive Control With Constraints
Maciejowski, J. M. (2001) · Cambridge University Engineering Department Publications Database · Cited by 4,305 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1285773 · Cited by 3,974 · see citing works
Predictive control with constraints
Camacho, E. F. (2003) · Automatica · doi:10.1016/s0005-1098(03)00056-6 · Cited by 3,546 · see citing works
Smart cities: Definitions, dimensions, performance, and initiatives
Albino, V., Berardi, U., & Dangelico, R. M. (2015) · Journal of Urban Technology · doi:10.1080/10630732.2014.942092 · Cited by 3,536 · see citing works
Farhangi, H. (2010) · IEEE Power and Energy Magazine · doi:10.1109/mpe.2009.934876 · Cited by 2,967 · see citing works
Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads
Pálenský, P., & Dietrich, D. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2158841 · Cited by 2,959 · see citing works
The Internet of Things: How the Next Evolution of the Internet Is Changing Everything
Evans, D. (2011) · Cited by 2,685 · see citing works
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2012-01703-3 · Cited by 2,424 · see citing works
Demand response and smart grids – a survey
Siano, P. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.10.022 · Cited by 2,411 · see citing works
Current trends in Smart City initiatives: Some stylised facts
Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., & Scorrano, F. (2014) · Cities · doi:10.1016/j.cities.2013.12.010 · Cited by 2,322 · see citing works
4th Generation District Heating (4GDH)
Lund, H., Werner, S., Wiltshire, R., Svendsen, S., Thorsen, J. E., Hvelplund, F., & Mathiesen, B. V. (2014) · Energy · doi:10.1016/j.energy.2014.02.089 · Cited by 2,273 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2023) · Figshare · doi:10.6084/m9.figshare.5203696.v2 · Cited by 2,088 · see citing works
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995 · see citing works
Machine learning methods for solar radiation forecasting: A review
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017) · Renewable Energy · doi:10.1016/j.renene.2016.12.095 · Cited by 1,836 · see citing works
Can we open the black box of AI?
Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773 · see citing works
Engineering Design via Surrogate Modelling: A Practical Guide
Keane, A. J., Forrester, A. I. J., & Sóbester, A. (2008) · American Institute of Aeronautics and Astronautics eBooks · doi:10.2514/4.479557 · Cited by 1,716 · see citing works
Comparative Environmental Life Cycle Assessment of Conventional and Electric Vehicles
Hawkins, T. R., Singh, B., Majeau‐Bettez, G., & Strømman, A. H. (2013) · Journal of Industrial Ecology · doi:10.1111/j.1530-9290.2012.00532.x · Cited by 1,670 · see citing works
Smart sustainable cities of the future: An extensive interdisciplinary literature review
Bibri, S. E., & Krogstie, J. (2017) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.02.016 · Cited by 1,599 · see citing works
MES (multi-energy systems): An overview of concepts and evaluation models
Mancarella, P. (2013) · Energy · doi:10.1016/j.energy.2013.10.041 · Cited by 1,440 · see citing works
Aitzhan, N. Z., & Svetinovic, D. (2018) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2616861 · Cited by 1,297 · see citing works
A Smart City Initiative: the Case of Barcelona
Bakıcı, T., Almirall, E., & Wareham, J. (2013) · Journal of the Knowledge Economy · doi:10.1007/s13132-012-0084-9 · Cited by 1,163 · see citing works
Potential of lithium-ion batteries in renewable energy
Diouf, B., & Pode, R. (2015) · Renewable Energy · doi:10.1016/j.renene.2014.11.058 · Cited by 1,120 · see citing works
The role of big data in smart city
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E., & Chiroma, H. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.05.002 · Cited by 1,103 · see citing works
Optimal Power Flow of Multiple Energy Carriers
Geidl, M., & Andersson, G. (2007) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.888988 · Cited by 1,077 · see citing works
Smart Energy Systems for coherent 100% renewable energy and transport solutions
Mathiesen, B. V., Lund, H., Connolly, D., Wenzel, H., Østergaard, P. A., Möller, B., Nielsen, S., Ridjan, I., Karnøe, P., Sperling, K., et al. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.01.075 · Cited by 1,067 · see citing works
Everything you wanted to know about smart cities: the Internet of Things is the backbone
Mohanty, S. P., Choppali, U., & Kougianos, E. (2016) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2016.2556879 · Cited by 1,056 · see citing works
Ensemble learning for data stream analysis: A survey
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050 · see citing works
Raza, M. Q., & Khosravi, A. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.065 · Cited by 1,036 · see citing works
A review on time series forecasting techniques for building energy consumption
Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.085 · Cited by 1,032 · see citing works
Smart cities as corporate storytelling
Söderström, O., Paasche, T. F., & Klauser, F. (2014) · City · doi:10.1080/13604813.2014.906716 · Cited by 1,007 · see citing works
Transfer learning using computational intelligence: A survey
Jie Lu, B., Behbood, V., Peng, H., Zuo, H., Xue, S., & Zhang, G. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.01.010 · Cited by 971 · see citing works
Applications of big data to smart cities
Al Nuaimi, E., Al Neyadi, H., Mohamed, N., & Al-Jaroodi, J. (2015) · Journal of Internet Services and Applications · doi:10.1186/s13174-015-0041-5 · Cited by 960 · see citing works
A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms
Vardakas, J. S., Zorba, N., & Verikoukis, C. V. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2341586 · Cited by 951 · see citing works
Silva, B. N., Khan, M., & Han, K.-J. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.01.053 · Cited by 903 · see citing works
Shaikh, P. H., Nor, N. M., Nallagownden, P., Elamvazuthi, I., & Ibrahim, T. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.03.027 · Cited by 876 · see citing works
Dounis, A. I., & Caraiscos, C. (2009) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2008.09.015 · Cited by 851 · see citing works
Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco
Lee, J. H., Hancock, M. G., & Hu, M. (2013) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2013.08.033 · Cited by 843 · see citing works
Big data driven smart energy management: from big data to big insights
Zhou, K., Fu, C., & Yang, S. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.050 · Cited by 841 · see citing works
Urban rainwater harvesting systems: Research, implementation and future perspectives
Campisano, A., Butler, D., Ward, S., et al. (2017) · Water Research · doi:10.1016/j.watres.2017.02.056 · Cited by 763 · see citing works
Rahman, A., Srikumar, V., & Smith, A. D. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.12.051 · Cited by 748 · see citing works
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
Dai, X., & Gao, Z. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2013.2243743 · Cited by 732 · see citing works
Current status and future advances for wind speed and power forecasting
Jung, J., & Broadwater, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.12.054 · Cited by 710 · see citing works
SmartSantander: IoT experimentation over a smart city testbed
Sánchez, L., Muñoz, L., Galache, J. A., Sotres, P., Santana, J. R., Gutiérrez, V., Ramdhany, R., Gluhak, A., Krčo, S., Theodoridis, E., et al. (2014) · Computer Networks · doi:10.1016/j.bjp.2013.12.020 · Cited by 707 · see citing works
Optimization in microgrids with hybrid energy systems – A review
Fathima, A. H., & Palanisamy, K. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.01.059 · Cited by 691 · see citing works
Mining big data: current status, and forecast to the future
Fan, W., & Bifet, A. (2013) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2481244.2481246 · Cited by 677 · see citing works
Security and privacy in smart city applications: Challenges and solutions
Zhang, K., Ni, J., Yang, K., Liang, X., Ren, J., & Shen, X. S. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600267cm · Cited by 638 · see citing works
Khurana, H., Hadley, M. D., Lü, N., & Frincke, D. A. (2010) · IEEE Security & Privacy · doi:10.1109/msp.2010.49 · Cited by 610 · see citing works
Principles and criteria for assessing urban energy resilience: a literature review
Sharifi, A., & Yamagata, Y. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.03.028 · Cited by 592 · see citing works
Advances and trends of energy storage technology in Microgrid
Tan, X., Li, Q., & Wang, H. (2012) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2012.07.015 · Cited by 591 · see citing works
Smart meters for power grid: Challenges, issues, advantages and status
Depuru, S. S. S. R., Wang, L., & Devabhaktuni, V. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2011.02.039 · Cited by 570 · see citing works
Smarter cities and their innovation challenges
Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., & Morris, R. (2011) · Computer · doi:10.1109/mc.2011.187 · Cited by 567 · see citing works
Security issues in SCADA networks
Igure, V. M., Laughter, S. A., & Williams, R. D. (2006) · Computers & Security · doi:10.1016/j.cose.2006.03.001 · Cited by 566 · see citing works
Advanced Lectures on Machine Learning
Bousquet, O., Rätsch, G., & Luxburg, U. (2004) · Lecture notes in computer science · doi:10.1007/b100712 · Cited by 565 · see citing works
How to strategize smart cities: Revealing the SMART model
Letaïfa, S. B. (2015) · Journal of Business Research · doi:10.1016/j.jbusres.2015.01.024 · Cited by 529 · see citing works
State of the art of thermal storage for demand-side management
Arteconi, A., Hewitt, N., & Polonara, F. (2012) · Applied Energy · doi:10.1016/j.apenergy.2011.12.045 · Cited by 515 · see citing works
Electric Vehicle Charging on Residential Distribution Systems: Impacts and Mitigations
Dubey, A., & Santoso, S. (2015) · IEEE Access · doi:10.1109/access.2015.2476996 · Cited by 485 · see citing works
Privacy concerns in smart cities
van Zoonen, L. (2016) · Government Information Quarterly · doi:10.1016/j.giq.2016.06.004 · Cited by 474 · see citing works
Putting the 'smarts' into the smart grid
Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R. (2012) · Communications of the ACM · doi:10.1145/2133806.2133825 · Cited by 465 · see citing works
Attia, S., Hamdy, M., O’Brien, W., et al. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.01.016 · Cited by 459 · see citing works
On heat pumps in smart grids: A review
Fischer, D. S., & Madani, H. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.11.182 · Cited by 446 · see citing works
Siddaiah, R., & Saini, R. P. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.281 · Cited by 429 · see citing works
A Game-Theoretic Approach to Energy Trading in the Smart Grid
Wang, Y., Saad, W., Han, Z., Poor, H. V., & Başar, T. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2284664 · Cited by 420 · see citing works
Deep Neural Network Based Demand Side Short Term Load Forecasting
Ryu, S., Noh, J.-K., & Kim, H. (2016) · Energies · doi:10.3390/en10010003 · Cited by 414 · see citing works
Arani, A. A. K., Karami, H., Gharehpetian, G. B., & Hejazi, M. A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.11.166 · Cited by 407 · see citing works
Domestic demand-side management (DSM): Role of heat pumps and thermal energy storage (TES) systems
Arteconi, A., Hewitt, N., & Polonara, F. (2012) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2012.09.023 · Cited by 392 · see citing works
Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
Alam, F., Mehmood, R., Katib, I. A., Albogami, N., & Albeshri, A. A. (2017) · IEEE Access · doi:10.1109/access.2017.2697839 · Cited by 389 · see citing works
Hilty, L. M., Arnfalk, P., Erdmann, L., Goodman, J., Lehmann, M., & Wäger, P. A. (2006) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2006.05.007 · Cited by 388 · see citing works
A review of fault detection and diagnostics methods for building systems
Kim, W., & Katipamula, S. (2017) · Science and Technology for the Built Environment · doi:10.1080/23744731.2017.1318008 · Cited by 388 · see citing works
Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings
Chae, Y. T., Horesh, R., Hwang, Y., & Lee, Y. M. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.11.045 · Cited by 386 · see citing works
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges
Mohammadi, M., & Al-Fuqaha, A. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700298 · Cited by 377 · see citing works
Smart sustainable cities – Exploring ICT solutions for reduced energy use in cities
Kramers, A., Höjer, M., Lövehagen, N., & Wangel, J. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2013.12.019 · Cited by 373 · see citing works
An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments
Pan, J., Jain, R., Paul, S., Vu, T., Saifullah, A., & Sha, M. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2413397 · Cited by 367 · see citing works
Smart grids, smart users? The role of the user in demand side management
Goulden, M., Bedwell, B., Rennick‐Egglestone, S., et al. (2014) · Energy Research & Social Science · doi:10.1016/j.erss.2014.04.008 · Cited by 357 · see citing works
Urban energy–water nexus: A network perspective
Chen, S., & Chen, B. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.03.042 · Cited by 356 · see citing works
Security and privacy challenges in smart cities
Braun, T., Fung, B. C. M., Iqbal, F., & Shah, B. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.02.039 · Cited by 354 · see citing works
Daut, M. A. M., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., & Hussin, F. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.12.015 · Cited by 347 · see citing works
Energy–nutrients–water nexus: Integrated resource recovery in municipal wastewater treatment plants
Mo, W., & Zhang, Q. (2013) · Journal of Environmental Management · doi:10.1016/j.jenvman.2013.05.007 · Cited by 336 · see citing works
Privacy Protection for Preventing Data Over-Collection in Smart City
Li, Y., Dai, W., Ming, Z., & Meikang Qiu, A. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2470247 · Cited by 321 · see citing works
Integration of decentralized energy systems in neighbourhoods using the energy hub approach
Orehounig, K., Evins, R., & Dorer, V. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.04.114 · Cited by 318 · see citing works
Distributed Energy Trading: The Multiple-Microgrid Case
Gregoratti, D., & Matamoros, J. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2352592 · Cited by 311 · see citing works
European smart cities: The role of zero energy buildings
Kylili, A., & Fokaides, P. A. (2015) · Sustainable Cities and Society · doi:10.1016/j.scs.2014.12.003 · Cited by 309 · see citing works
Reka, S. S., & Dragicevic, T. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.089 · Cited by 304 · see citing works
Fostering participaction in smart cities: a geo-social crowdsensing platform
Cardone, G., Foschini, L., Bellavista, P., Antonio Corradi, A., Borcea, C., Talasila, M., & Curtmola, R. (2013) · IEEE Communications Magazine · doi:10.1109/mcom.2013.6525603 · Cited by 299 · see citing works
Operational optimization and demand response of hybrid renewable energy systems
Wang, X., Palazoğlu, A., & El‐Farra, N. H. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.01.004 · Cited by 291 · see citing works
Optimal management of energy hubs and smart energy hubs – A review
Mohammadi, M., Noorollahi, Y., Mohammadi‐Ivatloo, B., et al. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.02.035 · Cited by 288 · see citing works
A review of energy systems models in the UK: Prevalent usage and categorisation
Hall, L. M. H., & Buckley, A. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.02.044 · Cited by 283 · see citing works
Integrated Demand Side Management Game in Smart Energy Hubs
Sheikhi, A., Rayati, M., Bahrami, S., & Ranjbar, A. M. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2377020 · Cited by 274 · see citing works
Future challenges for smart cities: Cyber-security and digital forensics
Baig, Z. A., Szewczyk, P., Valli, C., Rabadia, P., Hannay, P., Chernyshev, M., Johnstone, M., Kerai, P., Ibrahim, A., Sansurooah, K., et al. (2017) · Digital Investigation · doi:10.1016/j.diin.2017.06.015 · Cited by 274 · see citing works
Fallah, N., Deo, R. C., Shojafar, M., Conti, M., & Shamshirband, S. (2018) · Energies · doi:10.3390/en11030596 · Cited by 255 · see citing works
Linkage analysis for the water–energy nexus of city
Fang, D., & Chen, B. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.020 · Cited by 253 · see citing works
Nair, S., George, B. A., Malano, H. M., et al. (2014) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2014.05.007 · Cited by 251 · see citing works
Li, J., Fang, J., Zeng, Q., et al. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.10.054 · Cited by 250 · see citing works
Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method
Liang, J., & Du, R. (2007) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2006.12.012 · Cited by 250 · see citing works
Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering
Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. E. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2098369 · Cited by 250 · see citing works
Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings
Manic, M., Wijayasekara, D., Amarasinghe, K., & Rodriguez-Andina, J. J. (2016) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2015.2513749 · Cited by 250 · see citing works
Mavromatidis, G., Orehounig, K., & Carmeliet, J. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.02.021 · Cited by 238 · see citing works
Lu, Y., Wang, S., & Shan, K. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.06.007 · Cited by 232 · see citing works
Transfer learning with seasonal and trend adjustment for cross-building energy forecasting
Ribeiro, M., Grolinger, K., ElYamany, H. F., Higashino, W. A., & Capretz, M. A. M. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.01.034 · Cited by 227 · see citing works
Wu, R., Mavromatidis, G., Orehounig, K., et al. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.161 · Cited by 223 · see citing works
Energy Management for Smart Grids With Electric Vehicles Based on Hierarchical MPC
Kennel, F., Görges, D., & Liu, S. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2228876 · Cited by 220 · see citing works
Patteeuw, D., Bruninx, K., Arteconi, A., Delarue, E. D., D’haeseleer, W. D., & Helsen, L. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.04.014 · Cited by 216 · see citing works
Trends of European research and development in district heating technologies
Sayegh, M. A., Danielewicz, J., Nannou, T., et al. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.02.023 · Cited by 213 · see citing works
Prediction of electric vehicle charging-power demand in realistic urban traffic networks
Arias, M. B., Kim, M., & Bae, S. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.02.021 · Cited by 210 · see citing works
Smart City, Safety and Security
Lacinák, M., & Ristvej, J. (2017) · Procedia Engineering · doi:10.1016/j.proeng.2017.06.090 · Cited by 205 · see citing works
Reinforcement learning for energy conservation and comfort in buildings
Dalamagkidis, K., Kolokotsa, D. D., Kalaitzakis, K. C., & Stavrakakis, G. S. (2006) · Building and Environment · doi:10.1016/j.buildenv.2006.07.010 · Cited by 203 · see citing works
Hu, Z., Tian, P.-L., Li, S.-W., & Zhang, J. (2017) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.08.007 · Cited by 201 · see citing works
Gray-box modeling and validation of residential HVAC system for control system design
Afram, A., & Janabi‐Sharifi, F. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.10.026 · Cited by 200 · see citing works
Rogge, M., Wollny, S., & Sauer, D. U. (2015) · Energies · doi:10.3390/en8054587 · Cited by 198 · see citing works
1Distributed Energy Trading: The Multiple-Microgrid CasePreprint
Gregoratti, D., & Matamoros, J. (2016) · Cited by 195 · see citing works
Yang, R., & Wang, L. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.10.025 · Cited by 192 · see citing works
Prosumers in district heating networks – A Swedish case study
Brange, L., Englund, J., & Lauenburg, P. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.12.020 · Cited by 190 · see citing works
Demand-response in building heating systems: A Model Predictive Control approach
Bianchini, G., Casini, M., Vicino, A., & Zarrilli, D. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.01.088 · Cited by 189 · see citing works
Towards a smart energy network: The roles of fuel/electrolysis cells and technological perspectives
Zhang, X., Chan, S. H., Ho, H. K., Tan, S.-C., Li, M., Li, G., Li, J., & Feng, Z. (2015) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2015.03.133 · Cited by 188 · see citing works
A distributed predictive control approach to building temperature regulation
Ma, Y., Anderson, G., & Borrelli, F. (2011) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2011.5991549 · Cited by 187 · see citing works
Baeten, B., Rogiers, F., & Helsen, L. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.03.055 · Cited by 186 · see citing works
Maroufmashat, A., Elkamel, A., Fowler, M. W., et al. (2015) · Energy · doi:10.1016/j.energy.2015.10.079 · Cited by 173 · see citing works
Su, W., & Chow, M. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.11.088 · Cited by 171 · see citing works
Mocanu, E., Nguyen, P. H., Kling, W. L., & Gibescu, M. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.01.030 · Cited by 171 · see citing works
Li, Y., Rezgui, Y., & Zhu, H. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.01.061 · Cited by 165 · see citing works
Grey-box modelling and identification using physical knowledge and bayesian techniques
Tulleken, H. J. A. F. (1993) · Automatica · doi:10.1016/0005-1098(93)90124-c · Cited by 164 · see citing works
Optimizing rainwater harvesting systems for the dual purposes of water supply and runoff capture
Sample, D. J., & Liu, J. (2014) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2014.03.075 · Cited by 160 · see citing works
Klein, K. M., Herkel, S., Henning, H., & Felsmann, C. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.06.073 · Cited by 159 · see citing works
MongoDB-Based Repository Design for IoT-Generated RFID/Sensor Big Data
Kang, Y.-S., Park, I.-H., Rhee, J., & Lee, Y.-H. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2483499 · Cited by 156 · see citing works
Robust Fuzzy Fault-Tolerant Control of Wind Energy Conversion Systems Subject to Sensor Faults
Kamal, E., Aïtouche, A., Ghorbani, R., et al. (2012) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2011.2178105 · Cited by 148 · see citing works
Ruiz-Romero, S., Colmenar‐Santos, A., Mur, F., et al. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.05.082 · Cited by 144 · see citing works
Stochastic Model Predictive Control for Building Climate Control
Oldewurtel, F., Jones, C. N., Parisio, A., & Morari, M. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2013.2272178 · Cited by 143 · see citing works
Multicriteria optimization of a distributed energy supply system for an industrial area
Buoro, D., Casisi, M., Nardi, A. D., et al. (2013) · Energy · doi:10.1016/j.energy.2012.12.003 · Cited by 134 · see citing works
Optimization of multi-source complex district heating network, a case study
Vesterlund, M., Toffolo, A., & Dahl, J. (2017) · Energy · doi:10.1016/j.energy.2017.03.018 · Cited by 125 · see citing works
Fuel cell cars in a microgrid for synergies between hydrogen and electricity networks
Alavi, F., Lee, E. P., van de Wouw, N., et al. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.10.084 · Cited by 123 · see citing works
Oldenbroek, V., Verhoef, L. A., & van Wijk, A. (2017) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2017.01.155 · Cited by 119 · see citing works
Morvaj, B., Evins, R., & Carmeliet, J. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.058 · Cited by 117 · see citing works
Nocerino, R., Colorni, A., Lia, F., et al. (2016) · Transportation research procedia · doi:10.1016/j.trpro.2016.05.267 · Cited by 113 · see citing works
Issues in identification of control-oriented thermal models of zones in multi-zone buildings
Lin, Y., Middelkoop, T., & Barooah, P. (2012) · doi:10.1109/cdc.2012.6425958 · Cited by 112 · see citing works
Modelling temperature in intelligent buildings by means of autoregressive models
Ríos-Moreno, J. G., Trejo-Perea, M., Castañeda-Miranda, R., Hernández-Guzmán, V. M., & Herrera-Ruíz, G. (2007) · Automation in Construction · doi:10.1016/j.autcon.2006.11.003 · Cited by 111 · see citing works
Retail planning and urban resilience – An introduction to the special issue
Salgueiro, T. B., & Erkip, F. (2013) · Cities · doi:10.1016/j.cities.2013.01.007 · Cited by 104 · see citing works
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 · see citing works
Mosannenzadeh, F., Nucci, M. R. D., & Vettorato, D. (2017) · Energy Policy · doi:10.1016/j.enpol.2017.02.007 · Cited by 103 · see citing works
Qualitative indicators for smart city business models: The case of mobile services and applications
Walravens, N. (2015) · Telecommunications Policy · doi:10.1016/j.telpol.2014.12.011 · Cited by 101 · see citing works
Demand Side Management via prosumer interactions in a smart city energy marketplace
Karnouskos, S. (2011) · doi:10.1109/isgteurope.2011.6162818 · Cited by 98 · see citing works
Zero Energy Buildings: A Critical Look at the Definition; Preprint
Torcellini, P., Pless, S., Deru, M. P., et al. (2006) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 97 · see citing works
Heating control schemes for energy management in buildings
Paris, B., Eynard, J., Grieu, S., et al. (2010) · Energy and Buildings · doi:10.1016/j.enbuild.2010.05.027 · Cited by 96 · see citing works
Model-based predictive control applied to multi-carrier energy systems
Arnold, M., Negenborn, R. R., Andersson, G., et al. (2009) · IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pes.2009.5275230 · Cited by 94 · see citing works
Jing, R., Wang, M., Liang, H., et al. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.151 · Cited by 94 · see citing works
Persson, U., & Münster, M. (2016) · Energy · doi:10.1016/j.energy.2015.12.074 · Cited by 90 · see citing works
Smart Cities:an overview of the technology trends driving smart cities
Lea, R. (2017) · Lancaster EPrints (Lancaster University) · Cited by 89 · see citing works
Flynn, C., & Sirén, K. (2015) · Renewable Energy · doi:10.1016/j.renene.2015.03.036 · Cited by 85 · see citing works
Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms
Mavromatidis, G., Acha, S., & Shah, N. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.03.020 · Cited by 84 · see citing works
Self-citationCooperative Energy Trading in CoMP Systems Powered by Smart Grids
Xu, J., & Zhang, R. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2423317 · Cited by 83 · see citing works
Multi-zone building energy management using intelligent control and optimization
Yang, R., & Wang, L. (2012) · Sustainable Cities and Society · doi:10.1016/j.scs.2012.07.001 · Cited by 82 · see citing works
Smart grid communication technologies- overview, research challenges and opportunities
Baimel, D., Tapuchi, S., & Baimel, N. (2016) · doi:10.1109/speedam.2016.7526014 · Cited by 82 · see citing works
(2013) · Routledge eBooks · doi:10.4324/9780203066782 · Cited by 81 · see citing works
Demand side management for city districts
Müller, D., Monti, A., Stinner, S., Schlösser, T., Schütz, T., Matthes, P., Wolisz, H., Molitor, C., Harb, H., & Streblow, R. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.03.026 · Cited by 79 · see citing works
People-centric computing and communications in smart cities
Delmastro, F., Arnaboldi, V., & Conti, M. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7509389 · Cited by 79 · see citing works
Control and simulation of a flywheel energy storage for a wind diesel power system
Sebastián, R., & Alzola, R. P. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.08.017 · Cited by 72 · see citing works
Malinowski, P. A., Stillwell, A. S., Wu, J. S., et al. (2015) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000528 · Cited by 69 · see citing works
Hayes, J. G., & Goodarzi, G. A. (2018) · doi:10.1002/9781119063681 · Cited by 66 · see citing works
Fault Detection Based on Statistical Multivariate Analysis and Microarray Visualization
Ma, M.-D., Wong, D. S., Jang, S., & Tseng, S. (2009) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2009.2030793 · Cited by 65 · see citing works
The Shaping of Ambient Intelligence and the Internet of Things
Bibri, S. E. (2015) · Atlantis ambient and pervasive intelligence · doi:10.2991/978-94-6239-142-0 · Cited by 65 · see citing works
Habibzadeh, H., Boggio-Dandry, A., Qin, Z., Soyata, T., Kantarci, B., & Mouftah, H. T. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700304 · Cited by 64 · see citing works
Smart city dashboard for integrating various data of sensor networks
Suakanto, S., Supangkat, S. H., Suhardi, S., et al. (2013) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/ictss.2013.6588063 · Cited by 63 · see citing works
A multi-level energy performance diagnosis method for energy information poor buildings
Yan, C., Wang, S., Xiao, F., et al. (2015) · Energy · doi:10.1016/j.energy.2015.02.014 · Cited by 62 · see citing works
Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations
Javaid, N., Hussain, S. M., Ullah, I., Noor, M. A., Abdul, W., Almogren, A., & Alamri, A. M. (2017) · Energies · doi:10.3390/en10081131 · Cited by 60 · see citing works
Huang, Z., Lu, Y., Wei, M., et al. (2017) · Energy · doi:10.1016/j.energy.2017.11.093 · Cited by 59 · see citing works
Ten questions concerning smart districts
Good, N., Ceseña, E. A. M., & Mancarella, P. (2017) · Building and Environment · doi:10.1016/j.buildenv.2017.03.037 · Cited by 55 · see citing works
Daianova, L., Dotzauer, E., Thorin, E., et al. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.016 · Cited by 54 · see citing works
Olama, M. M., Kuruganti, T. P., Nutaro, J. J., & Dong, J. (2018) · Energies · doi:10.3390/en11071852 · Cited by 49 · see citing works
Assessing demand response with heat pumps for efficient grid operation in smart grids
Kreuder, L., & Spataru, C. (2015) · Sustainable Cities and Society · doi:10.1016/j.scs.2015.07.011 · Cited by 45 · see citing works
Holistic modelling techniques for the operational optimisation of multi-vector energy systems
Reynolds, J. F., Ahmad, M. W., & Rezgui, Y. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.03.065 · Cited by 44 · see citing works
Urban energy systems: An integrated approach
Revel, D. (2013) · OpenEdition (OpenEdition) · doi:10.58079/oa4f · Cited by 42 · see citing works
Smart Cities, Green Technologies, and Intelligent Transport Systems
Helfert, M., Krempels, K.-H., Klein, C., et al. (2015) · Communications in computer and information science · doi:10.1007/978-3-319-27753-0 · Cited by 38 · see citing works
Competition for Water Resources: Experiences and Management Approaches in the US and Europe
Ziolkowska, J. R., & Peterson, J. M. (2016) · Cited by 37 · see citing works
Liang, Q., Han, H., Cui, X., et al. (2017) · Science and Technology for the Built Environment · doi:10.1080/23744731.2017.1375012 · Cited by 34 · see citing works
Modelling and disturbance estimation for model predictive control in building heating systems
O’Dwyer, E., Tommasi, L. D., Kouramas, K. I., et al. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.08.077 · Cited by 33 · see citing works
Self-citationControl of electric vehicles fast charging station supplied by PV/energy storage system/grid
García–Triviño, P., RAMIREZ, L. M. F., Torreglosa, J. P., & Jurado, F. (2016) · IEEE International Energy Conference (ENERGYCON) · doi:10.1109/energycon.2016.7514120 · Cited by 30 · see citing works
Active actuator fault detection and diagnostics in HVAC systems
Weimer, J. E., Ahmadi, S. A., Araújo, J., et al. (2012) · doi:10.1145/2422531.2422551 · Cited by 29 · see citing works
Optimal Partitioning for the Decentralized Thermal Control of Buildings
Chandan, V., & Alleyne, A. G. (2012) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2012.2219308 · Cited by 27 · see citing works
Controlling electric vehicle charging points for congestion management of UK LV networks
Petrou, K., Quirós‐Tortós, J., & Ochoa, L. F. (2015) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt.2015.7131843 · Cited by 26 · see citing works
Prioritised objectives for model predictive control of building heating systems
O’Dwyer, E., Tommasi, L. D., Kouramas, K. I., Cychowski, M. T., & Lightbody, G. (2017) · Control Engineering Practice · doi:10.1016/j.conengprac.2017.03.018 · Cited by 25 · see citing works
Self-citationPARK: A privacy-preserving aggregation scheme with adaptive key management for smart grid
Zhang, K., Lu, R., Liang, X., Qiao, J., & Shen, X. (2013) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccchina.2013.6671121 · Cited by 24 · see citing works
Bustos-Turu, G., van Dam, K. H., Acha, S., et al. (2016) · IEEE International Energy Conference (ENERGYCON) · doi:10.1109/energycon.2016.7514077 · Cited by 22 · see citing works
Self-citationEstimating plug-in electric vehicle demand flexibility through an agent-based simulation model
Bustos-Turu, G., van Dam, K. H., Acha, S., et al. (2014) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2014.7028889 · Cited by 20 · see citing works
Self-citationZhang, Q., Ishihara, K. N., McLellan, B. C., et al. (2012) · International Journal of Energy Research · doi:10.1002/er.2948 · Cited by 19 · see citing works
López, A. M., Sánchez, L., Doctor, F., et al. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1398270 · Cited by 19 · see citing works
Acha, S., Bustos-Turu, G., & Shah, N. (2016) · IEEE International Energy Conference (ENERGYCON) · doi:10.1109/energycon.2016.7514090 · Cited by 19 · see citing works
Self-citationParameter-Invariant Monitor Design for Cyber–Physical Systems
Weimer, J., Ivanov, R., Chen, S., Roederer, A., Sokolsky, O., & Lee, I. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2723847 · Cited by 18 · see citing works
Michiorri, A., Girard, R., Kariniotakis, G., et al. (2012) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2012.6465667 · Cited by 17 · see citing works
Nice Grid: Smart grid pilot demonstrating innovative distribution network operation
Lannez, S., Foggia, G., Muscholl, M., et al. (2013) · doi:10.1109/ptc.2013.6652365 · Cited by 13 · see citing works
Optimisation of Wastewater Treatment and Recovery Solutions in Industrial Parks
O’Dwyer, E., Wang, H., Wang, A., Shah, N., & Guo, M. (2018) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-444-64235-6.50246-1 · Cited by 13 · see citing works
Self-citationGames and teams with shared constraints
Kulkarni, A. A. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0302 · Cited by 11 · see citing works
Real option valuation of a decremental regulation service provided by electricity storage
Szabó, D. Z., & Martyr, R. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0300 · Cited by 10 · see citing works
The Water–Energy Nexus in Europe and Spain
Villamayor‐Tomás, S. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-803237-4.00006-9 · Cited by 8 · see citing works
Parameter-invariant detection of unknown inputs in networked systems
Weimer, J. E., Varagnolo, D., Stanković, M. S., et al. (2013) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2013.6760563 · Cited by 7 · see citing works
Gajare, S., Rao, J. G., Naidu, O. D., et al. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0295 · Cited by 6 · see citing works
Demand and supply side management strategies for zero energy buildings
Ghiasi, M. I., Hajizadeh, A., Golkar, M. A., et al. (2017) · IEEE International Conference on Ubiquitous Wireless Broadband · doi:10.1109/icuwb.2017.8251007 · Cited by 6 · see citing works
Energy management: flexibility, risk and optimization
Moriarty, J., & Mancarella, P. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2017.0052 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756 · see citing works
Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115733 · Cited by 327 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Zhu, H., Goh, H. H., Zhang, D., Ahmad, T., Liu, H., Wang, S., Li, S., Liu, T., Dai, H., & Wu, T. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129809 · Cited by 131 · see citing works
Coordinated energy management for a cluster of buildings through deep reinforcement learning
Pinto, G., Piscitelli, M. S., Vázquez-Canteli, J. R., Nagy, Z., & Capozzoli, A. (2021) · Energy · doi:10.1016/j.energy.2021.120725 · Cited by 108 · see citing works
- Authors
- O’Dwyer, E., Pan, I., Acha, S., & Shah, N.
- Affiliations
- Imperial College London (GB)
- The Alan Turing Institute (GB)
- Corresponding author
- Pan, Indranil
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 237
- Pages
- 581-597
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2910531128
- Cited by
- 410
- References
- 202
- Citations by year
- 2026: 29 · 2025: 62 · 2024: 75 · 2023: 55 · 2022: 51 · 2021: 64 · 2020: 56 · 2019: 18
- FWCI
- 16.99
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- European Union’s Horizon 2020 (691895)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Keywords
- smart energy systems, smart cities, energy management, computational intelligence, energy integration, sensor networks, hierarchy of controls, multi-actor decision-making, sustainable urban energy
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
