When artificial intelligence meets building energy efficiency, a review focusing on zero energy building
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[1] Natasa Nord,et al. Influence of occupant behavior and operation on performance of a residential Zero Emission Building in Norway , 2018 .
[2] Kazem Zare,et al. Risk-based scheduling of smart apartment building under market price uncertainty using robust optimization approach , 2019, Sustainable Cities and Society.
[3] Nora El-Gohary,et al. A review of data-driven building energy consumption prediction studies , 2018 .
[4] Ugo Fiore,et al. Planning and operational energy optimization solutions for smart buildings , 2019, Inf. Sci..
[5] Priyanka Singh,et al. Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem , 2018 .
[6] Jason J. Jung,et al. Social big data: Recent achievements and new challenges , 2015, Information Fusion.
[7] Sungmin Yoon,et al. Strategies for virtual in-situ sensor calibration in building energy systems , 2018, Energy and Buildings.
[8] T. Esbensen,et al. Dimensioning of the solar heating system in the zero energy house in Denmark , 1977 .
[9] N. Jones. How to stop data centres from gobbling up the world’s electricity , 2018, Nature.
[10] Jing Zhao,et al. A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis , 2018, Energy and Buildings.
[11] A. Immanuel Selvakumar,et al. Optimal sizing of photovoltaic/battery/diesel based hybrid system and optimal tilting of solar array using the artificial intelligence for remote houses in India , 2015 .
[12] Declan Butler,et al. Architecture: Architects of a low-energy future , 2008, Nature.
[13] Balaji Kalluri,et al. Optimal Sensor Placement Strategy for Office Buildings Using Clustering Algorithms , 2018 .
[14] Chuanjian Yang,et al. Incremental reduction algorithm with acceleration strategy based on conflict region , 2019, Artif. Intell. Rev..
[15] Xu Chen,et al. A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction , 2018, Energy and Buildings.
[16] Emmanuel Rey,et al. Integrated thinking for photovoltaics in buildings , 2018, Nature Energy.
[17] Pradeep Kumar Mallick,et al. The Internet of Things: Insights into the building blocks, component interactions, and architecture layers , 2018 .
[18] Wei Yan,et al. A study of the impact of occupant behaviors on energy performance of building envelopes using occupants’ data , 2017 .
[19] Zeyu Wang,et al. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models , 2017 .
[20] Jui-Sheng Chou,et al. Modeling heating and cooling loads by artificial intelligence for energy-efficient building design , 2014 .
[21] Maher Kayal,et al. Cooperative energy management of a community of smart-buildings: A Blockchain approach , 2020 .
[22] Joyce Kim,et al. Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning , 2018 .
[23] R. Dowling,et al. The material politics of smart building energy management: A view from Sydney's commercial office space , 2019, Political Geography.
[24] Eike Musall,et al. From Low-Energy to Net Zero-Energy Buildings: Status and Perspectives , 2011 .
[26] Haoxiang Wang,et al. Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings , 2017, Future Gener. Comput. Syst..
[27] Zhang Shi-con. Research on Definition Development and Main Content of Zero Energy Building , 2013 .
[28] Geoffrey Qiping Shen,et al. Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement , 2019 .
[29] Abbas Khosravi,et al. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .
[30] Alán Aspuru-Guzik,et al. Accelerating the discovery of materials for clean energy in the era of smart automation , 2018, Nature Reviews Materials.
[31] Javier Contreras,et al. Medium-term energy hub management subject to electricity price and wind uncertainty , 2016 .
[32] Yang Liu,et al. Coordinating the operations of smart buildings in smart grids , 2018 .
[33] Mohammad Reza Mohammadi,et al. Energy hub: From a model to a concept – A review , 2017 .
[34] Magali A. Delmas,et al. Correction: Corrigendum: The effectiveness of US energy efficiency building labels , 2017 .
[35] S. M. Hakimi,et al. Intelligent energy management in off-grid smart buildings with energy interaction , 2020 .
[36] Juan M. Corchado,et al. IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings , 2020, Future Gener. Comput. Syst..
[37] Navid Delgarm,et al. Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC) , 2016 .
[38] Lisa Guan,et al. Design a Zero Energy House in Brisbane, Australia , 2015 .
[39] Marian Verhelst,et al. A Review on Internet of Things Solutions for Intelligent Energy Control in Buildings for Smart City Applications , 2017 .
[40] Yu Wang,et al. Distributed aggregation control of grid-interactive smart buildings for power system frequency support , 2019, Applied Energy.
[41] Chirag Deb,et al. Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings , 2018 .
[42] Ram Rajagopal,et al. Data-driven planning of distributed energy resources amidst socio-technical complexities , 2017, Nature Energy.
[43] Yuan Jin,et al. Modeling occupancy and behavior for better building design and operation—A critical review , 2018, Building Simulation.
[44] Bin Xu,et al. Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation , 2018 .
[45] Bernard Souyri,et al. Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies , 2015 .
[46] Zhang Shengyua. ENLIGHTENMENT FROM GERMAN BUILDING ENERGY EFFICIENCY STANDARD EVOLUTION , 2010 .
[47] H. R. Kranz. Initial and Continuous Commissioning of Building Automation and Control Systems (BACS) -Preview EN ISO 16484- , 2008 .
[48] Houda Najeh,et al. Diagnosis of sensor grids in a building context: Application to an office setting , 2018 .
[49] Mustafa Inalli,et al. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system , 2008 .
[50] Murad Khan,et al. A generic internet of things architecture for controlling electrical energy consumption in smart homes , 2018, Sustainable Cities and Society.
[51] Baizhan Li,et al. Occupants’ behavioural adaptation in workplaces with non-central heating and cooling systems , 2012 .
[52] Michael B. Tobey,et al. Modeling and design of smart buildings , 2020 .
[53] Matthew R. Shaner,et al. Net-zero emissions energy systems , 2018, Science.
[54] Mohammad Soheil Ghobadi,et al. Probabilistic seismic collapse and residual drift assessment of smart buildings equipped with shape memory alloy connections , 2019, Engineering Structures.
[55] Zahra Pezeshki,et al. Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey , 2018, Artificial Intelligence Review.
[56] El Madjid Berkouk,et al. Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study , 2017 .
[57] Jean Hennebert,et al. Big Building Data - a Big Data Platform for Smart Buildings , 2017 .
[58] Jensen Zhang,et al. Energy-saving building system integration with a smart and low-cost sensing/control network for sustainable and healthy living environments: Demonstration case study , 2020 .
[59] P. Torcellini,et al. Understanding Zero-Energy Buildings , 2006 .
[60] Sung-Han Sim,et al. Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning , 2019 .
[61] Derong Liu,et al. Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming , 2018, IEEE/CAA Journal of Automatica Sinica.
[62] Mark Gillott,et al. A review of occupant-centric building control strategies to reduce building energy use , 2018, Renewable and Sustainable Energy Reviews.
[63] B. Sovacool,et al. Smart Home Technologies in Europe: A Critical Review of Concepts, Benefits, Risks and Policies , 2019, Renewable and Sustainable Energy Reviews.
[64] Natalija Koseleva,et al. Big Data in Building Energy Efficiency: Understanding of Big Data and Main Challenges , 2017 .
[65] Mohammad Yusri Hassan,et al. Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review , 2017 .
[66] Wil L. Kling,et al. Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network , 2014, ArXiv.
[67] M. Chester,et al. Energy efficiency to reduce residential electricity and natural gas use under climate change , 2017, Nature Communications.
[68] Yong Shi,et al. A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .
[69] Haiyan Lu,et al. Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting , 2017 .
[70] Vítor Leal,et al. Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior , 2017 .
[71] Marco Dorigo,et al. Swarm intelligence: from natural to artificial systems , 1999 .
[72] Jean-Paul M. G. Linnartz,et al. Propagating sensor uncertainty to better infer office occupancy in smart building control , 2018, Energy and Buildings.
[73] A. Jaffe. The role of the US in the geopolitics of climate policy and stranded oil reserves , 2016, Nature Energy.
[74] Miguel Molina-Solana,et al. Data science for building energy management: A review , 2017 .
[75] W. Feng,et al. Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050 , 2018, Nature Energy.
[76] Ding Jian,et al. Incremental reduction algorithm with acceleration strategy based on conflict region , 2017, Artificial Intelligence Review.
[77] Muhammad Aslam Uqaili,et al. Simulation tools application for artificial lighting in buildings , 2018 .
[78] Abderrahmane Baïri,et al. Environmental monitoring system based on an Open Source Platform and the Internet of Things for a building energy retrofit , 2018 .
[79] Eric Wai Ming Lee,et al. Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach , 2018, Applied Energy.
[80] Han Zhang,et al. Renewable energy: Present research and future scope of Artificial Intelligence , 2017 .
[81] Dimitris Vrakas,et al. Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation , 2018, Artificial Intelligence Review.
[82] Standard Ashrae. Thermal Environmental Conditions for Human Occupancy , 1992 .
[83] Chi Yung Jim,et al. Applying artificial intelligence modeling to optimize green roof irrigation , 2016 .
[84] Magali A. Delmas,et al. The effectiveness of US energy efficiency building labels , 2017 .
[85] Pieter de Wilde,et al. Predictability of occupant presence and performance gap in building energy simulation , 2017 .
[86] Alaa Chateauneuf,et al. Quantification of workmanship insulation defects and their impact on the thermal performance of building facades , 2016 .
[87] Dennis L. Loveday,et al. Artificial intelligence for buildings , 1992 .
[88] Jun Li,et al. Energy performance heterogeneity in China׳s buildings sector: A data-driven investigation , 2016 .
[89] Myoung-Souk Yeo,et al. Application of artificial neural network to predict the optimal start time for heating system in building , 2003 .
[90] A. Mahdavi,et al. IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings , 2017 .
[91] Jonghoon Ahn,et al. Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments , 2017 .
[92] 송태민. Social Big Data 기반 보건의료 연구방법론 , 2013 .
[93] Zheng O'Neill,et al. A review of smart building sensing system for better indoor environment control , 2019, Energy and Buildings.