Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes

Occupant comfort management is an important feature of a smart home, which requires achieving a high occupant comfort level as well as minimum energy consumption. Based on a large amount of data, learning models enable us to predict factors of a mathematical model for deriving the optimal result without expensive experiments. Comfort management supports high-level comfort to the occupant in the individual indoor environment, using the optimal power consumption to run home appliances. In this paper, we propose occupant comfort management based on energy optimization, using an environment prediction model. The proposed energy optimization model provides optimal power consumption based on the proposed objective function, which requires temperature and comfort index data as the input parameters. For the input requirement, temperature prediction model and humidity prediction model are presented based on a recurrent neural network with a pre-collected dataset, including indoor and outdoor temperature and humidity sensing data. Using the predicted temperature and humidity data, the comfort index model derives the predicted mean vote value to be used in the energy optimization model with the predicted temperature data. The experimental results present an 8.43% reduction of the optimized power consumption compared to the actual power consumption using mean absolute percentage error to calculate. Moreover, the emulation of an indoor environment using optimal energy consumption presents as approximately similar to the actual data.

[1]  Xiao Chen,et al.  A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings , 2015 .

[2]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[3]  Do-Hyeun Kim,et al.  A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model , 2018 .

[4]  Mohammed Arif,et al.  Occupant productivity and office indoor environment quality: A review of the literature , 2016 .

[5]  Lingfeng Wang,et al.  Multi-agent control system with intelligent optimization for smart and energy-efficient buildings , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[6]  Jan Hensen,et al.  Thermal comfort in residential buildings: Comfort values and scales for building energy simulation , 2009 .

[7]  Daniel Nikovski,et al.  An IoT system to estimate personal thermal comfort , 2016, 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT).

[8]  S. Ari,et al.  A practical approach to individual thermal comfort and energy optimization problem , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[9]  Yunpeng Wang,et al.  Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks , 2017, Sensors.

[10]  Zhiwei Yang,et al.  Existing building retrofitting for Indoor PM2.5 Concentration Control on Smog Days: Case study in China , 2017 .

[11]  Nursyarizal Mohd Nor,et al.  Stochastic optimized intelligent controller for smart energy efficient buildings , 2014 .

[12]  Mauro Cannistraro,et al.  Smart Control of Air Climatization System in Function on the Values of Mean Local Radiant Temperature , 2015 .

[13]  Eugénio Rodrigues,et al.  A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment , 2017 .

[14]  Yangyang Shi,et al.  RNN-based labeled data generation for spoken language understanding , 2015, INTERSPEECH.

[15]  Jun Wang,et al.  Financial time series prediction by a random data-time effective RBF neural network , 2014, Soft Comput..

[16]  Jürgen Schmidhuber,et al.  Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks , 2007, IJCAI.

[17]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[18]  H. Sebastian Seung,et al.  Natural Image Denoising with Convolutional Networks , 2008, NIPS.

[19]  Farshad Kowsary,et al.  Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO) , 2016 .

[20]  Do-Hyeun Kim,et al.  An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes , 2017 .

[21]  Tao Ding,et al.  Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach , 2018, Energies.

[22]  Lingfeng Wang,et al.  Optimal control strategy for HVAC system in building energy management , 2012, PES T&D 2012.

[23]  Tomasz Grychowski,et al.  Fuzzy inference system for the assessment of indoor environmental quality in a room , 2017 .

[24]  E. Lorenzini,et al.  The Applications of the New Technologies "E-Sensing" in Hospitals , 2016 .

[25]  Burcin Becerik-Gerber,et al.  Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort , 2016 .

[26]  Lingfeng Wang,et al.  Multi-agent intelligent controller design for smart and sustainable buildings , 2010, 2010 IEEE International Systems Conference.

[27]  Jennie Malboeuf.  Algorithm , 1994, Neurology.

[28]  Atila Novoselac,et al.  Appliance daily energy use in new residential buildings: Use profiles and variation in time-of-use , 2014 .

[29]  Umar,et al.  WSN-Based Smart Sensors and Actuator for Power Management in Intelligent Buildings , 2015 .

[30]  G. Cannistraro,et al.  Some observations on the radiative exchanges influence on thermal comfort in rectangular open-space environments , 2015 .

[31]  Jun Wang,et al.  Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations , 2016 .

[32]  Alberto Bemporad,et al.  Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities , 2018 .

[33]  O. Tetens,et al.  Uber einige meteorologische begriffe , 1930 .

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

[35]  Yang Geng,et al.  The impact of thermal environment on occupant IEQ perception and productivity , 2017 .

[36]  Ismail Güvenç,et al.  IoT-based occupancy monitoring techniques for energy-efficient smart buildings , 2015, 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[37]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[38]  Bjarne W. Olesen,et al.  Introduction to thermal comfort standards and to the proposed new version of EN ISO 7730 , 2002 .

[39]  Nursyarizal Mohd Nor,et al.  A review on optimized control systems for building energy and comfort management of smart sustainable buildings , 2014 .

[40]  Alex Graves,et al.  Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.

[41]  Krishna R. Pattipati,et al.  Predicting individual thermal comfort using machine learning algorithms , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[42]  Barry P. Haynes,et al.  Workplace productivity and office type: An evaluation of office occupier differences based on age and gender , 2017 .

[43]  Anastasios I. Dounis,et al.  Building visual comfort control with fuzzy reasoning , 1993 .

[44]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[45]  Georgios Sermpinis,et al.  Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects , 2014 .

[46]  V. I. Hanby,et al.  System optimization for HVAC energy management using the robust evolutionary algorithm. , 2009 .

[47]  Roberto Lamberts,et al.  A review of human thermal comfort in the built environment , 2015 .

[48]  Lingfeng Wang,et al.  Multi-agent control system with information fusion based comfort model for smart buildings , 2012 .

[49]  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 .

[50]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[51]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[52]  Joyce Kim,et al.  Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control , 2018 .

[53]  H. N. Lam,et al.  Using genetic algorithms to optimize controller parameters for HVAC systems , 1997 .

[54]  Mauro Cannistraro,et al.  THE LOCAL MEDIA RADIANT TEMPERATURE FOR THE CALCULATION OF COMFORT IN AREAS CHARACTERIZED BY RADIANT SURFACES , 2015 .

[55]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[56]  Joseph G. Allen,et al.  Green Buildings and Health , 2015, Current Environmental Health Reports.

[57]  Anastasios I. Dounis,et al.  Intelligent Coordinator of Fuzzy Controller-Agents for Indoor Environment Control in Buildings Using 3-D Fuzzy Comfort Set , 2007, 2007 IEEE International Fuzzy Systems Conference.

[58]  Massimiliano Manfren,et al.  Building Automation and Control Systems and performance optimization: A framework for analysis , 2017 .

[59]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[60]  Manoj Tripathy.  Power transformer differential protection using neural network Principal Component Analysis and Radial Basis Function Neural Network , 2010, Simul. Model. Pract. Theory.

[61]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[62]  Matjaz Gams,et al.  Trade-off between Energy Consumption and Comfort Experience in Smart Buildings , 2015, Inf. Technol. Control..

[63]  Ali Emadi,et al.  Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[64]  Yung-Cheol Byun,et al.  Customer Prediction on Parking Logs Using Recurrent Neural Network , 2018, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

[65]  Milos Manic,et al.  Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings , 2016, IEEE Industrial Electronics Magazine.

[66]  Liu Yang,et al.  Thermal comfort and building energy consumption implications - A review , 2014 .

[67]  Mauro Cannistraro,et al.  Islands "Smart Energy" for eco-sustainable energy a case study "Favignana Island" , 2017 .