A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings

Abstract Building air-conditioning and mechanical ventilation (ACMV) systems are responsible for significant energy consumption and yet, dissatisfaction with the thermal environment is prevalent among the occupants, revealing a widespread disparity between energy-efficiency and indoor thermal-comfort in buildings. This paper presents an indoor-climate control framework that bridges this gap between energy and comfort. The framework comprises two main components: a thermal-comfort prediction model, and an optimization algorithm termed as the optimal air temperature (OAT) algorithm; they collectively act as an intelligent mediator between the occupant and the ACMV system. Firstly, the ACMV energy consumption is modelled as a function of air temperature, and three operating frequencies of cooling components using a feedforward neural network. Secondly, the thermal-comfort prediction model predicts the thermal state index (TSI: Cool-Discomfort/Comfort/Warm-Discomfort). Thirdly, depending on the predicted TSI, the OAT algorithm locates the optimal operating state such that Comfort state is achieved using the minimum ACMV energy consumption. Proposed framework exhibits an energy saving potential of 36.5%. It is found that 25 °C is the ideal air temperature for desired comfort with minimum energy expense in the tropical buildings. Additionally, six different TSI predictive models including two general and four personal comfort models are implemented to validate the framework. The study is substantiated with extensive real human experiments in controlled thermal environment. The proposed method is scalable for its applicability with any comfort-prediction model, and adaptive for its data-driven architecture. It exhibits the potential to achieve both occupant-comfort and energy-saving through integration with the Internet-of-Things for realizing comfort-energy balanced buildings.

[1]  B. Becerik-Gerber,et al.  Energy consequences of Comfort-driven temperature setpoints in office buildings , 2018, Energy and Buildings.

[2]  Ralph E.H. Sims,et al.  Recognising the potential for renewable energy heating and cooling , 2008 .

[3]  P. Fanger,et al.  Extension of the PMV model to non-air-conditioned buildings in warm climates , 2002 .

[4]  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 .

[5]  Edward Arens,et al.  Air Quality and Thermal Comfort in Office Buildings: Results of a Large Indoor Environmental Quality Survey , 2006 .

[6]  Lihua Xie,et al.  Thermal comfort prediction using normalized skin temperature in a uniform built environment , 2018 .

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

[8]  Burcin Becerik-Gerber,et al.  Towards unsupervised learning of thermal comfort using infrared thermography , 2018 .

[9]  S. Sekhar,et al.  Thermal comfort in air-conditioned buildings in hot and humid climates--why are we not getting it right? , 2016, Indoor air.

[10]  Javier Tarrío-Saavedra,et al.  Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data , 2017 .

[11]  Guoqing Liu,et al.  A pilot study of online non-invasive measuring technology based on video magnification to determine skin temperature , 2017 .

[12]  Lihua Xie,et al.  Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology , 2018 .

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

[14]  Jesús M. Zamarreño,et al.  Energy savings and guaranteed thermal comfort in hotel rooms through nonlinear model predictive controllers , 2016 .

[15]  Chao Huan,et al.  Optimization of room air temperature in stratum-ventilated rooms for both thermal comfort and energy saving , 2017 .

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

[17]  Yeng Chai Soh,et al.  Modeling and optimization of different sparse Augmented Firefly Algorithms for ACMV systems under two case studies , 2017 .

[18]  Lihua Xie,et al.  Machine learning based prediction of thermal comfort in buildings of equatorial Singapore , 2017, 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC).

[19]  Hua Li,et al.  On assuming Mean Radiant Temperature equal to air temperature during PMV-based thermal comfort study in air-conditioned buildings , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[20]  M. Hancock,et al.  Do people like to feel ‘neutral’?: Exploring the variation of the desired thermal sensation on the ASHRAE scale , 2007 .

[21]  Tarik Kousksou,et al.  Energy consumption and efficiency in buildings: current status and future trends , 2015 .

[22]  Hua Li,et al.  Convolutional Neural Network and Kernel Methods for Occupant Thermal State Detection using Wearable Technology , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[23]  Hui Zhang,et al.  The skin's role in human thermoregulation and comfort , 2006 .

[24]  Wei Gu,et al.  Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers , 2018, Applied Energy.

[25]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[26]  Siaw Kiang Chou,et al.  Achieving better energy-efficient air conditioning - A review of technologies and strategies , 2013 .

[27]  Farrokh Jazizadeh,et al.  Personalized thermal comfort inference using RGB video images for distributed HVAC control , 2018, Applied Energy.

[28]  Lillykutty Jacob,et al.  A flexible control strategy for energy and comfort aware HVAC in large buildings , 2018 .

[29]  R. Yao,et al.  A theoretical adaptive model of thermal comfort – Adaptive Predicted Mean Vote (aPMV) , 2009 .

[30]  Jintu Fan,et al.  Personal thermal management using portable thermoelectrics for potential building energy saving , 2018 .

[31]  B. W. Ang,et al.  Quantifying drivers of CO2 emissions from electricity generation – Current practices and future extensions , 2018, Applied Energy.

[32]  V. Ismet Ugursal,et al.  Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector , 2008 .