Overview of the use of artificial neural networks for energy‐related applications in the building sector

[1]  Wil L. Kling,et al.  Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network , 2014, ArXiv.

[2]  Antonio Messineo,et al.  Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building , 2014 .

[3]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[4]  Guohai Liu,et al.  Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis , 2015 .

[5]  Fredrik Wallin,et al.  An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks , 2017 .

[6]  G. C. Bakos,et al.  Modelling of small scale central heating installation using artificial neural networks aiming at low electric energy consumption , 2013 .

[7]  Manuel P. Cuéllar,et al.  Energy consumption forecasting based on Elman neural networks with evolutive optimization , 2018, Expert Syst. Appl..

[8]  Tuğçe Kazanasmaz,et al.  Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation , 2014 .

[9]  Nuria Forcada,et al.  Implementation of predictive control in a commercial building energy management system using neural networks , 2017 .

[10]  Jonghoon Ahn,et al.  Performance analysis of space heating smart control models for energy and control effectiveness in five different climate zones , 2017 .

[11]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[12]  Ji-Hyun Lee,et al.  Determining optimum control of double skin envelope for indoor thermal environment based on artificial neural network , 2014 .

[13]  Kwang Ho Lee,et al.  Application of artificial neural networks for determining energy-efficient operating set-points of the VRF cooling system , 2017 .

[14]  Gerardo Maria Mauro,et al.  Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach , 2017 .

[15]  Federico Silvestro,et al.  Electrical consumption forecasting in hospital facilities: An application case , 2015 .

[16]  Lei Chen,et al.  A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings , 2015 .

[17]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[18]  Jin Woo Moon,et al.  Performance of ANN-based predictive and adaptive thermal-control methods for disturbances in and around residential buildings , 2012 .

[19]  Jose I. Bilbao,et al.  A review and analysis of regression and machine learning models on commercial building electricity load forecasting , 2017 .

[20]  P. Frey,et al.  Artificial Neural Network modelling of sorption chillers , 2014 .

[21]  Merih Aydinalp,et al.  Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks , 2002 .

[22]  Jin Yang,et al.  On-line building energy prediction using adaptive artificial neural networks , 2005 .

[23]  Sooyoung Kim,et al.  Prediction models and control algorithms for predictive applications of setback temperature in cooling systems , 2017 .

[24]  Vittorio Cesarotti,et al.  Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study , 2016 .

[25]  Jan Dimon Bendtsen,et al.  Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms , 2017 .

[26]  Hwataik Han,et al.  Simplified dynamic neural network model to predict heating load of a building using Taguchi method , 2016 .

[27]  Merih Aydinalp,et al.  Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks , 2004 .

[28]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[29]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[30]  Jui-Sheng Chou,et al.  Modeling heating and cooling loads by artificial intelligence for energy-efficient building design , 2014 .

[31]  Fernanda Leite,et al.  An applied artificial intelligence approach towards assessing building performance simulation tools , 2008 .

[32]  Manish Mishra,et al.  Performance prediction of solid desiccant – Vapor compression hybrid air-conditioning system using artificial neural network , 2016 .

[33]  Ji-Hyun Lee,et al.  Performance evaluation of artificial neural network-based variable control logic for double skin enveloped buildings during the heating season , 2014 .

[34]  Rodney A. Stewart,et al.  ANN-based residential water end-use demand forecasting model , 2013, Expert Syst. Appl..

[35]  Jin Woo Moon,et al.  ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms , 2015 .

[36]  Jian Chu,et al.  Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A compa , 2011 .

[37]  Eric Wai Ming Lee,et al.  A study of the importance of occupancy to building cooling load in prediction by intelligent approach , 2011 .

[38]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[39]  Leopold,et al.  Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region , 2016 .

[40]  Kristen S. Cetin,et al.  Modeling urban building energy use: A review of modeling approaches and procedures , 2017 .

[41]  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 .

[42]  Betul Bektas Ekici,et al.  Prediction of building energy consumption by using artificial neural networks , 2009, Adv. Eng. Softw..

[43]  Cinzia Buratti,et al.  An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks , 2014 .

[44]  Yacine Rezgui,et al.  Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .

[45]  Xueqing Zhang,et al.  A comprehensive review on the application of artificial neural networks in building energy analysis , 2019, Neurocomputing.

[46]  Tin-Tai Chow,et al.  The use of occupancy space electrical power demand in building cooling load prediction , 2012 .

[47]  Hrvoje Krstić,et al.  Application of neural networks in predicting airtightness of residential units , 2014 .

[48]  Ryohei Yokoyama,et al.  Prediction of energy demands using neural network with model identification by global optimization , 2009 .

[49]  Tomasz Prauzner,et al.  Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks , 2017 .

[50]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[51]  Jonghoon Ahn,et al.  Development of an intelligent building controller to mitigate indoor thermal dissatisfaction and peak energy demands in a district heating system , 2017 .

[52]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

[53]  A. Kanarachos,et al.  Multivariable control of single zone hydronic heating systems with neural networks , 1998 .

[54]  S. Renganarayanan,et al.  Modelling of steam fired double effect vapour absorption chiller using neural network , 2006 .

[55]  Michaël Kummert,et al.  A neural network controller for hydronic heating systems of solar buildings , 2004, Neural Networks.

[56]  Maria del Carmen Pegalajar Jiménez,et al.  An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings , 2016 .

[57]  Jin Woo Moon,et al.  Development of an artificial neural network model based thermal control logic for double skin envelopes in winter , 2013 .

[58]  Miguel Molina-Solana,et al.  Data science for building energy management: A review , 2017 .

[59]  Ming Zhong,et al.  Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining , 2016 .

[60]  J. C. Bruno,et al.  Inverse neural network based control strategy for absorption chillers , 2012 .

[61]  Manuel R. Arahal,et al.  A prediction model based on neural networks for the energy consumption of a bioclimatic building , 2014 .

[62]  Joaquim Melendez,et al.  Short-term load forecasting in a non-residential building contrasting models and attributes , 2015 .

[63]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[64]  Servet Soyguder,et al.  Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for , 2011 .

[65]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[66]  Cinzia Buratti,et al.  Comparison of the Energy Performance of Existing Buildings by Means of Dynamic Simulations and Artificial Neural Networks , 2016 .

[67]  Eric Wai Ming Lee,et al.  An intelligent approach to assessing the effect of building occupancy on building cooling load predi , 2011 .

[68]  Qinmin M. Yang,et al.  Simultaneous control of indoor air temperature and humidity for a chilled water based air conditioning system using neural networks , 2016 .

[69]  Mehrdad Boroushaki,et al.  Design and construction of a non-linear model predictive controller for building's cooling system , 2018 .

[70]  Myoung-Souk Yeo,et al.  Application of artificial neural network to predict the optimal start time for heating system in building , 2003 .

[71]  Rajesh Kumar,et al.  Energy analysis of a building using artificial neural network: A review , 2013 .

[72]  Sousso Kelouwani,et al.  Comparison and Simulation of Building Thermal Models for Effective Energy Management , 2015 .

[73]  Gerardo Maria Mauro,et al.  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 , 2017 .

[74]  Radiša Jovanović,et al.  Ensemble of various neural networks for prediction of heating energy consumption , 2015 .

[75]  Giuliano Dall'O',et al.  Application of neural networks for evaluating energy performance certificates of residential buildings , 2016 .

[76]  François Boudéhenn,et al.  Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation , 2016 .

[77]  Carlos Rubio-Bellido,et al.  Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions , 2017 .

[78]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[79]  Evgueniy Entchev,et al.  Performance prediction of a solar thermal energy system using artificial neural networks , 2014 .

[80]  Jin Woo Moon,et al.  Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model , 2016 .

[81]  D. Gossard,et al.  Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network , 2013 .

[82]  Fan Zhang,et al.  A review on time series forecasting techniques for building energy consumption , 2017 .

[83]  Kaamran Raahemifar,et al.  Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system , 2017 .

[84]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[85]  István Farkas,et al.  Neural network modelling of thermal stratification in a solar DHW storage , 2010 .

[86]  Giuseppina Ciulla,et al.  Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy , 2017 .

[87]  Paulo Carreira,et al.  Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks , 2017 .

[88]  Zeyu Wang,et al.  A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models , 2017 .

[89]  M. P. Cuéllar,et al.  Parallel memetic algorithm for training recurrent neural networks for the energy efficiency problem , 2019, Appl. Soft Comput..

[90]  Ömer Akgöbek,et al.  The prediction of convective heat transfer in floor-heating systems by artificial neural networks ☆ , 2008 .

[91]  Jin Woo Moon,et al.  Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings , 2016 .

[92]  Esteban Jove,et al.  Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization , 2019, Energy.

[93]  Francisco J. Batlles,et al.  Performance study of solar-assisted air-conditioning system provided with storage tanks using artificial neural networks , 2011 .

[94]  Youngdeok Hwang,et al.  Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings , 2016 .

[95]  A. S. Dalkılıç,et al.  A novel ANN-based approach to estimate heat transfer coefficients in radiant wall heating systems , 2017 .

[96]  Yong Shi,et al.  A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .

[97]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[98]  Tony Roskilly,et al.  A review of building climate and plant controls, and a survey of industry perspectives , 2018 .

[99]  Vivek Srikumar,et al.  Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks , 2018 .

[100]  Jong-Jin Kim,et al.  ANN-based thermal control models for residential buildings , 2010 .