$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

[해외논문] ANNEXE: An open-source building energy design optimisation framework using artificial neural networks and genetic algorithms

Journal of cleaner production, v.371, 2022년, pp.133500 -   

García Kerdan, Iván ,  Morillón Gálvez, David

초록이 없습니다.

참고문헌 (64)

  1. Sol. Energy Afrand 188 83 2019 10.1016/j.solener.2019.05.080 Energy and exergy analysis of two novel hybrid solar photovoltaic geothermal energy systems incorporating a building integrated photovoltaic thermal system and an earth air heat exchanger system 

  2. Energy Build. Ahmad 165 301 2018 10.1016/j.enbuild.2018.01.017 A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: a review 

  3. Therm. Sci. Eng. Prog. Arshad 9 308 2019 10.1016/j.tsep.2018.12.008 Energy and exergy analysis of fuel cells: a review 

  4. Build. Environ. Asadi 56 370 2012 10.1016/j.buildenv.2012.04.005 A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB 

  5. Energy Build. Asadi 81 444 2014 10.1016/j.enbuild.2014.06.009 Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application 

  6. Energy Build. Ascione 146 200 2017 10.1016/j.enbuild.2017.04.069 CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: a new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building 

  7. 2004 ANSI/ASHRAE Standard 55-2004. Thermal Environmental Conditions for Human Occupancy 

  8. Energy Convers. Manag. Ashraf 250 2021 10.1016/j.enconman.2021.114913 Strategic-level performance enhancement of a 660 MWe supercritical power plant and emissions reduction by AI approach 

  9. Energy Build. Attia 60 110 2013 10.1016/j.enbuild.2013.01.016 Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design 

  10. Energy Build. Azari 168 225 2018 10.1016/j.enbuild.2018.03.003 Embodied energy of buildings: a review of data, methods, challenges, and research trends 

  11. Energy Baklacioglu 86 709 2015 10.1016/j.energy.2015.04.025 Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks 

  12. Appl. Energy Beltrán 306 2022 10.1016/j.apenergy.2021.118049 Framework for collaborative intelligence in forecasting day-ahead electricity price 

  13. Energies Bonetti 10 1 95 2017 10.3390/en10010095 Dynamic exergy analysis for the thermal storage optimization of the building envelope 

  14. Appl. Therm. Eng. Byrne 149 414 2019 10.1016/j.applthermaleng.2018.12.069 Exergy analysis of heat pumps for simultaneous heating and cooling 

  15. J. Clean. Prod. Chen 254 2020 10.1016/j.jclepro.2019.119866 Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings 

  16. AIChE J. del Rio-Chanona 65 3 915 2019 10.1002/aic.16473 Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design 

  17. ECB-Annex37. Technical Synthesis Report: Low Exergy Systems for Heating and Cooling of Buildings, IEA ECBCS. In: Jagpal R, editor. UK2007. 

  18. 2011 Detailed Exergy Assessment Guidebook for the Built Environment, IEA ECBCS 

  19. 1278 2012 EnergyPlus. EnergyPlus Engineering Reference 

  20. Buildings Evola 8 12 180 2018 10.3390/buildings8120180 Exergy analysis of energy systems in buildings 

  21. Renew. Sustain. Energy Rev. Fathi 133 2020 10.1016/j.rser.2020.110287 Machine learning applications in urban building energy performance forecasting: a systematic review 

  22. Appl. Energy Gao 238 320 2019 10.1016/j.apenergy.2019.01.032 Building information modelling based building energy modelling: a review 

  23. Appl. Energy García Kerdan 280 2020 10.1016/j.apenergy.2020.115862 Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building 

  24. Energy Build. García Kerdan 133 155 2016 10.1016/j.enbuild.2016.09.029 An exergoeconomic-based parametric study to examine the effects of active and passive energy retrofit strategies for buildings 

  25. Energy García Kerdan 128 244 2017 10.1016/j.energy.2017.03.142 A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design 

  26. Build. Environ. García Kerdan 117 100 2017 10.1016/j.buildenv.2017.03.003 Thermodynamic and exergoeconomic analysis of a non-domestic Passivhaus retrofit 

  27. Appl. Energy García Kerdan 192 33 2017 10.1016/j.apenergy.2017.02.006 ExRET-Opt: an automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation 

  28. Build. Environ. García Kerdan 155 224 2019 10.1016/j.buildenv.2019.03.015 Thermodynamic and thermal comfort optimisation of a coastal social house considering the influence of the thermal breeze 

  29. Renew. Sustain. Energy Rev. Gasparatos 13 5 956 2009 10.1016/j.rser.2008.03.005 Assessing the sustainability of the UK society using thermodynamic concepts: Part 2 

  30. Energy Build. Gou 169 484 2018 10.1016/j.enbuild.2017.09.095 Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand 

  31. Proc. IME J. Power Energy Hammond 215 2 141 2001 10.1243/0957650011538424 Exergy analysis of the United Kingdom energy system 

  32. Renew. Sustain. Energy Rev. Hepbasli 16 1 73 2012 10.1016/j.rser.2011.07.138 Low exergy (LowEx) heating and cooling systems for sustainable buildings and societies 

  33. 2016 Building Design Optimization Using jEPlus 

  34. J. Therm. Anal. Calorim. Kalbasi 139 4 2913 2020 10.1007/s10973-019-09198-1 Improving performance of AHU using exhaust air potential by applying exergy analysis 

  35. Sustainability Lucero-Álvarez 8 7 590 2016 10.3390/su8070590 The effects of roof and wall insulation on the energy costs of low income housing in Mexico 

  36. Renew. Sustain. Energy Rev. Luo 131 2020 10.1016/j.rser.2020.109980 Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings 

  37. Energy Convers. Manag. Mahian 226 2020 10.1016/j.enconman.2020.113467 Exergy analysis in combined heat and power systems: a review 

  38. Energy Build. Malatji 61 81 2013 10.1016/j.enbuild.2013.01.042 A multiple objective optimisation model for building energy efficiency investment decision 

  39. McNeil Sc 2018 (USAID) USAfID Mexico space cooling electricity impacts and mitigation strategies: analysis supporting the summit on space cooling research needs and opportunities in Mexico 

  40. Energy Build. Molinari 63 119 2013 10.1016/j.enbuild.2013.03.050 The application of the parametric analysis for improved energy design of a ground source heat pump for residential buildings 

  41. Case Stud. Therm. Eng. Molliet 25 2021 10.1016/j.csite.2021.100972 Exergy analysis of the human body to assess thermal comfort conditions: comparison of the thermal responses of males and females 

  42. Sustain. Energy Technol. Assessments Nasruddin 35 48 2019 10.1016/j.seta.2019.06.002 Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm 

  43. Energy Build. Ngo 182 264 2019 10.1016/j.enbuild.2018.10.004 Early predicting cooling loads for energy-efficient design in office buildings by machine learning 

  44. Appl. Energy Perera 243 191 2019 10.1016/j.apenergy.2019.03.202 Machine learning methods to assist energy system optimization 

  45. J. Clean. Prod. Pham 260 2020 10.1016/j.jclepro.2020.121082 Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability 

  46. J. Build. Eng. Picallo-Perez 39 2021 Ventilation of buildings with heat recovery systems: thorough energy and exergy analysis for indoor thermal wellness 

  47. Appl. Energy Prada 225 814 2018 10.1016/j.apenergy.2018.04.129 On the performance of meta-models in building design optimization 

  48. Python_Software_Foundation. Python Language Reference Version 2.7. 

  49. Energy Reynolds 151 729 2018 10.1016/j.energy.2018.03.113 A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control 

  50. Appl. Energy Reynolds 235 699 2019 10.1016/j.apenergy.2018.11.001 Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm 

  51. Appl. Energy Röck 258 2020 10.1016/j.apenergy.2019.114107 Embodied GHG emissions of buildings - the hidden challenge for effective climate change mitigation 

  52. Appl. Energy Seyedzadeh 279 2020 10.1016/j.apenergy.2020.115908 Machine learning modelling for predicting non-domestic buildings energy performance: a model to support deep energy retrofit decision-making 

  53. J. Build. Eng. Sharif 25 2019 Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA 

  54. Shin 1617 2021 10.1016/B978-0-323-88506-5.50250-3 Computer Aided Chemical Engineering Deep learning and AutoML for dynamic modeling of LNG regasification process using seawater 

  55. Energy Build. Siddharth 43 10 2718 2011 10.1016/j.enbuild.2011.06.028 Automatic generation of energy conservation measures in buildings using genetic algorithms 

  56. Spangher 39 2020 Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities. Virtual Event Augmenting reinforcement learning with a planning model for optimizing energy demand response 

  57. Energy Build. Tian 158 1306 2018 10.1016/j.enbuild.2017.11.022 Towards adoption of building energy simulation and optimization for passive building design: a survey and a review 

  58. Energy Build. Utlu 35 11 1145 2003 10.1016/j.enbuild.2003.09.003 A study on the evaluation of energy utilization efficiency in the Turkish residential-commercial sector using energy and exergy analyses 

  59. Energy Build. Walker 209 2020 10.1016/j.enbuild.2019.109705 Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings 

  60. Energy Build. Westermann 198 170 2019 10.1016/j.enbuild.2019.05.057 Surrogate modelling for sustainable building design - a review 

  61. Appl. Energy Yang 156 577 2015 10.1016/j.apenergy.2015.07.050 Reinforcement learning for optimal control of low exergy buildings 

  62. Sustain. Cities Soc. Ye 42 176 2018 10.1016/j.scs.2018.05.050 Predicting electricity consumption in a building using an optimized back-propagation and Levenberg-Marquardt back-propagation neural network: case study of a shopping mall in China 

  63. Appl. Therm. Eng. Zhou 137 430 2018 10.1016/j.applthermaleng.2018.03.064 Evaluation of renewable energy utilization efficiency in buildings with exergy analysis 

  64. J. Clean. Prod. Zhou 254 2020 10.1016/j.jclepro.2020.120082 Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings 

활용도 분석정보

상세보기
다운로드
내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

관련 콘텐츠

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로