Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31440
A Comprehensive Review of Adaptive Building Energy Management Systems Based on Users’ Feedback

Authors: P. Nafisi Poor, P. Javid

Abstract:

Over the past few years, the idea of adaptive buildings and specifically, adaptive building energy management systems (ABEMS) has become popular. Well-performed management in terms of energy is to create a balance between energy consumption and user comfort; therefore, in new energy management models, efficient energy consumption is not the sole factor and the user's comfortability is also considered in the calculations. One of the main ways of measuring this factor is by analyzing user feedback on the conditions to understand whether they are satisfied with conditions or not. This paper provides a comprehensive review of recent approaches towards energy management systems based on users' feedbacks and subsequently performs a comparison between them premised upon their efficiency and accuracy to understand which approaches were more accurate and which ones resulted in a more efficient way of minimizing energy consumption while maintaining users' comfortability. It was concluded that the highest accuracy rate among the presented works was 95% accuracy in determining satisfaction and up to 51.08% energy savings can be achieved without disturbing user’s comfort. Considering the growing interest in designing and developing adaptive buildings, these studies can support diverse inquiries about this subject and can be used as a resource to support studies and researches towards efficient energy consumption while maintaining the comfortability of users.

Keywords: Adaptive buildings, energy efficiency, intelligent buildings, user comfortability.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 238

References:


[1] Kuru A., Oldfield P., Bonser S., Fiorito F., Biomimetic adaptive building skins: Energy and environmental regulation in building (2019).
[2] Humphreys M.A., Fergus Nicol J., Understanding the adaptive approach to thermal comfort (1998).
[3] Humphreys M.A., Fergus Nicol J., Adaptive thermal comfort and sustainable thermal standards for buildings (2002).
[4] Gauthier S., Bourikas L., Al-Atrash F., Bae C., Chun C., De Dear R., Hellwig R.T., Kim J., Kwon S., Mora R., Pandya H., Rawal R., Tartarini F., Upadhyay R., Wagner A., The colors of comfort: From thermal sensation to person-centric thermal zones for adaptive building strategies (2020).
[5] Daum D., Haldi F., Morel N., A personalized measure of thermal comfort for building controls (2010).
[6] Mineo H., Abe K., Tadanori M., An adaptive energy management system using heterogeneous sensor/actuator networks (2010).
[7] ASHRAE, Thermal environmental conditions for human occupancy, ASHRAE 2004.
[8] Sharples S., Malama A., A thermal comfort field survey in the cool season of Zambia (1997).
[9] Fishman D.S., Pimbert S.L., The thermal environment in offices (1982).
[10] Becker R., Paciuk M., Thermal comfort in residential building: Failure to predict by standard model (2009).
[11] Wong N.H., Khoo S.S., Thermal comfort in classrooms in the tropics (2003).
[12] Doherty T.J., Arens E., Evaluation of the physiological bases of thermal comfort models (1988).
[13] Parsons K.C., The effects of gender, acclimation state, the opportunity to adjust clothing and physical disability on requirements for thermal comfort (2002).
[14] Erickson V.L., Cerpa A.E., Thermovote: Participatory sensing for efficient building HVAC conditioning (2012).
[15] Carreira P., Costa A.A., Mansur V., Arsenio A., Can HVAC really learn from users? A simulation- based study on the effectiveness of voting for comfort and energy use optimization (2018).
[16] Tarantini M., Pernigotto G., Gasparella A., A co-citation analysis on thermal comfort and productivity aspects in production and office buildings (2017).
[17] Mofidi F., Akbari H., An integrated model for position-based productivity and energy costs optimization in offices (2019).
[18] Luo M., Zhou X., Zhu Y., Sundell J., Revisiting an overlooked parameter in thermal comfort studies, the metabolic rate (2016).
[19] Ormandy D., Ezratty V., Health and thermal comfort: from WHO guidance to housing strategies (2012).
[20] Sheikh Khan D., Kolarik J., Weitzmann P., Design and application of occupant voting systems for collecting occupant feedback on indoor environmental quality of buildings – a review (2020).
[21] West S. R., Ward J. K., Wall J., Trial results from a model predictive control and optimization system for commercial building HVAC (2014).
[22] Jazizadeh F., Marin F. M., Gerber B. B., A thermal preference scale for personalized comfort profile identification via participatory sensing (2013).
[23] Jung W., Jazizadeh F., Human in the loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions.
[24] Kim J., Zhou Y., Schiavon S., Raftery P., Brager G., Personal comfort models: Predicting individuals’ thermal preference using occupant heating and cooling behavior and machine learning (2018).
[25] Mason K., Grijalva S., A review of reinforcement learning for autonomous building energy management (2019).
[26] Barret E., Linder S., Autonomous HVAC control: A reinforcement learning approach (2015).
[27] American Society of Heating, Refrigeration and Air conditioning Engineers, ASHRAE handbook – Chapter 9. Thermal comfort (2017).
[28] Parsons KC., Human thermal environments (1993).
[29] Murakami Y., Terano M., Kana M., Masayuki H., Kuno S., Field experiment on energy consumption and thermal comfort in the office environment controlled by occupants’ requirements from PC terminal (2006).
[30] Costa A.A., Lopes P.M., Antunes A., Cabral I., Grilo A., Rodrigues F.M., 3I buildings: Intelligent, Interactive and Immersive Buildings (2015).
[31] Brooks J., Siddharth G., Subramany R., Lin Y., Middelkoop T., Arpan L., Carloni L., Barooah P., An experimental investigation of occupancy-based energy efficient control of commercial building indoor climate (2014).
[32] Goyal S., Ingley H., Barooah P., Occupancy-based zone climate control for energy efficient buildings: Complexity vs. performance (2013).
[33] Sierra E., Hossain A., Rodrigues D., Martinez M.G., Britos P., Martinez R.G., Optimizing building’s environments performance using intelligent systems (2008).
[34] Krainer A., Toward smart buildings (1996).
[35] McKay D., Information theory, interface and learning algorithms (2003).
[36] Purdon S., Kusy B., Jurdak R., Challen G., Model-free HVAC control using occupant feedback (2013).
[37] Lam A.H., Yuan Y., Wang D., An occupant-participatory approach for thermal comfort enhancement and energy conservation in buildings (2014).
[38] Jaziadeh F., Ghahramani A., Gerber B.B, Kichkaylo T., Orosz M., User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings (2014).
[39] Ghahramani A., Jazizadeh F., Gerber B.B., A knowledge- based approach for selecting energy-aware and comfort-driven HVAC temperature set points (2014).
[40] Li D., Menassa C.C., Kamat V.R., Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography (2018).
[41] Taylor G.I., The blood supply of the skin (1997).
[42] Ghahramani A., Tang C., Gerber B.B., An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling (2015).