RESEARCH ARTICLE

Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico

  • Pedro PONCE , 1 ,
  • Therese PEFFER 2 ,
  • Arturo MOLINA 1
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  • 1. Tecnologico de Monterrey, calle del puente 222 Mexico, Mexico City, Non-US/Non-Canadian 64849, Mexico
  • 2. California Institute for Energy and Environment, University of California Berkeley, Berkeley, CA 94720, USA

Received date: 29 Jun 2017

Accepted date: 28 Oct 2017

Published date: 15 Sep 2019

Copyright

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature

Abstract

It is well known that smart thermostats (STs) have become key devices in the implementation of smart homes; thus, they are considered as primary elements for the control of electrical energy consumption in households. Moreover, energy consumption is drastically affected when the end users select unsuitable STs or when they do not use the STs correctly. Furthermore, in future, Mexico will face serious electrical energy challenges that can be considerably resolved if the end users operate the STs in a correct manner. Hence, it is important to carry out an in-depth study and analysis on thermostats, by focusing on social aspects that influence the technological use and performance of the thermostats. This paper proposes the use of a signal detection theory (SDT), fuzzy detection theory (FDT), and chi-square (CS) test in order to understand the perceptions and beliefs of end users about the use of STs in Mexico. This paper extensively shows the perceptions and beliefs about the selected thermostats in Mexico. Besides, it presents an in-depth discussion on the cognitive perceptions and beliefs of end users. Moreover, it shows why the expectations of the end users about STs are not met. It also promotes the technological and social development of STs such that they are relatively more accepted in complex electrical grids such as smart grids.

Cite this article

Pedro PONCE , Therese PEFFER , Arturo MOLINA . Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico[J]. Frontiers in Energy, 2019 , 13(3) : 522 -538 . DOI: 10.1007/s11708-018-0562-2

Acknowledgments

This work was supported by “Bi-national laboratory for the intelligent management of energy sustainability and technological education” (Grant No. 266632) from CONACYT-SENER, Tecnologico de Monterrey, and UC Berkeley.
1
Darby S. Smart metering: what potential for householder engagement? Building Research and Information, 2010, 38(5): 442–457

DOI

2
Peffer T, Pritoni M, Meier A, Aragon C, Perry D. How people use thermostats in homes: a review. Building and Environment, 2011, 46(12): 2529–2541

DOI

3
Rosas J, Sheinbaum C, Morillon D. The structure of household energy consumption and related CO2 emissions by income group in Mexico. Energy for Sustainable Development, 2010, 14(2): 127–133

DOI

4
Turber S, vom Brocke J, Gassmann O, Fleisch E. Designing business models in the era of internet of things. In: Tremblay M C, VanderMeer D, Rothenberger M, Gupta A, Yoon V (eds), Advancing the Impact of Design Science: Moving from Theory to Practice. DESRIST 2014. Lecture Notes in Computer Science, vol 8463. Springer, Cham

DOI

5
Pritoni M, Woolley J, Modera M P. Do occupancy-responsive learning thermostats save energy? A field study in university residence halls. Energy and Buildings, 2016, 127: 469–478

DOI

6
Meier A, Aragon C, Peffer T, Perry D, Pritoni M. Usability of residential thermostats: preliminary investigations. Building and Environment, 2011, 46(10): 1891–1898

DOI

7
ENERGY.GOV. Energy saver: setting your thermostats for comfort and saving. 2016–09, available at energy.gov website

8
Kleiminger W, Mattern F, Santini S. Predicting household occupancy for smart heating control: a comparative performance analysis of state-of-the-art approaches. Energy and Building, 2014, 85: 493–505

DOI

9
Keshtkar A, Arzanpour S, Keshtkar F, Ahmadi P. Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives. Energy and Building, 2015, 104: 165–180

DOI

10
Mahmood A, Javaid N, Razzaq S. A review of wireless communications for smart grid. Renewable & Sustainable Energy Reviews, 2015, 41: 248–260

DOI

11
Shipworth M. Thermostat settings in English houses: no evidence of change between 1984 and 2007. Building and Environment, 2011, 46(3): 635–642

DOI

12
Maarten W. The research agenda on social acceptance of distributed generation in smart grids: renewable as common pool resources. Renewable & Sustainable Energy Reviews, 2012, 6(1): 822–835

DOI

13
Assefa G, Frostell B. Social sustainability and social acceptance in technology assessment: a case study of energy technologies. Technology in Society, 2007, 29(1): 6–78

DOI

14
Uncles R. What will lead to product acceptance and growing sales? In: Medical Equipment Industry-Potential for Growth Conference, 1998, 79–84

15
Ponce P, Polasko K, Molina A. Technology transfer motivation analysis based on fuzzy type-2 signal detection theory. AI & Society, 2016, 31(2): 245–257

DOI

16
McNicol D. A Primer of Signal Detection Theory. Hove: Psychology Press, 2005

17
Prasad S, Bruce L M. Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 625–629

DOI

18
Pritoni M, Meier A K, Aragon C, Perry D, Peffer T. Energy efficiency and the misuse of programmable thermostats: the effectiveness of crowdsourcing for understanding household behavior. Energy Research & Social Science, 2015, 8: 190–197

DOI

19
Cericola R.10 smart home apps that make you want the Apple Watch. 2015, available at electronichouse.com website

20
Hamdi M, Lachiver G. A fuzzy control system based on the human sensation of thermal comfort. In: IEEE World Congress on Computational Intelligence, IEEE International Conference on Fuzzy Systems Proceedings, Anchorage, AK, USA, 1998

DOI

21
Meyer J R, Uhrich D T. U.S. Patent No. 6101824, 2000

22
Boone R G, Gordon J, Barnes F, Fraser-Beekman S. Factors impacting innovation in a product development organization. In: IEEE International Conference on in Electro/Information Techno-logy (EIT), Indianapolis, IN, USA, 2012

DOI

23
Karjalainen S. Gender differences in thermal comfort and use of thermostats in everyday thermal environments. Building and Environment, 2007, 42(4): 1594–1603

DOI

24
Moon J W, Han S H. Thermostat strategies impact on energy consumption in residential buildings. Energy and Building, 2011, 43(2): 338–346

DOI

25
Nielsen J, Landauer T K. A mathematical model of the finding of usability problems. In: Proceedings of the INTERACT’93 and CHI’93 Conference on Human Factors in Computing Systems, Amsterdam, The Netherlands, 1993, 206–213

DOI

26
Nielsen J. Why you only need to test with 5 users: alertbox. 2016–10, available at nngroup.com website

27
Nielsen J. Usability Engineering. London: Elsevier, 1994

28
Faulkner L. Beyond the five-user assumption: benefits of increased sample sizes in usability testing. Behavior Research Methods, Instruments, & Computers, 2003, 35(3): 379–383

DOI PMID

29
Salvendy G, Lewis J R. Handbook of Human Factors and Ergonomics. London: Wiley, 2006

30
Mantel N. Chi-square tests with one degree of freedom; extensions of the Mantel-Haenszel procedure. Journal of the American Statistical Association, 1963, 58(303): 690–700

DOI

31
Conci M, Pianesi F, Zancanaro M. Useful, social and enjoyable: Mobile phone adoption by older people. In: INTERACT’09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I, 2009, 63–76

DOI

32
National Institute of Statistic and Geography. Población (statistical information about population in Mexico). 2017–09, available at beta.inegi.org.mx website

33
National Institute of Statistic and Geography. “Estadísticas a propósito del… día mundial de internet (17 de mayo)”datos nacionales (statistical information about technology Mexico). 2017–09, available at inegi.org.mx website

34
Campbell S W. Perceptions of mobile phones in college classrooms: ringing, cheating, and classroom policies. Communication Education, 2006, 55(3): 280–294

DOI

35
White M P, Eiser J R, Harris P R. Risk perceptions of mobile phone use while driving. Risk Analysis, 2004, 24(2): 323–334

DOI PMID

36
Macmillan N A. Signal detection theory. In: Stevens’ Handbook of Experimental Psychology. London: Wiley, 2002

37
Parasuraman R, Masalonis A J, Hancock P A. Fuzzy signal detection theory: basic postulates and formulas for analyzing human and machine performance. Human Factors, 2000, 42(4): 636–659

DOI PMID

38
Lancaster H O, Seneta E. Chi-square Distribution. London: John Wiley & Sons, Ltd, 1969

39
Özdemir T, Eyduran E. Comparison of chi-square and likelihood ratio chi-square tests: power of test. Journal of Applied Sciences Research, 2005, 1(2): 242–244

DOI

40
Lancaster H O, Seneta E. Chi-square Distribution. London: John Wiley & Sons, Ltd, 2005

41
Franke T M, Ho T, Christie C A. The chi-square test often used and more often misinterpreted. American Journal of Evaluation, 2012, 33(3): 448–458

DOI

42
Swets J A. Signal Detection and Recognition by Human Observers.New York: Wiley, 1964

43
Peterson W W, Birdsall T G, Fox W C. The theory of signal detectability. Proceedings of the IRE Professional Group on Information Theory, 1954, 4: 171–212

DOI

44
Marcum J I. A statistical theory of target detection by pulsed radar. IRE Transactions on Information Theory, 1960, 6(2): 259–267

DOI

45
Paredes-Olay C, Moreno-Fernández M M, Rosas J M, Ramos-Álvarez M M. ROC analysis in olive oil tasting: a signal detection theory approach to tasting tasks. Food Quality and Preference, 2010, 21(5): 562–568

DOI

46
Green D M, Swets J A. Signal Detection Theory and Psychophysics.New York: John Wiley & Sons Ltd., 1966

47
Macmillan N A, Douglas C C. Response bias: characteristics of detection theory, threshold theory, and “nonparametric” indexes. Psychological Bulletin, 1990, 107(3): 401–413

DOI

48
Snodgrass J G, Corwin J. Pragmatics of measuring recognition memory: applications to dementia and amnesia. Journal of Experimental Psychology: General, 1988, 117(1): 34–50

DOI PMID

49
Masalonis A, Parasuraman R. Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations. Ergonomics, 2003, 46(11): 1045–1074

DOI

50
Parasuraman R, Masalonis A J, Hancock P A. Fuzzy signal detection theory: basic postulates and formulas for analyzing human and machine performance. Human Factors, 2000, 42(4): 636–659

DOI PMID

51
Baptiste P, Trépanier S, Pireaux S, Quessy S. Difficulties connected to the integration of resource management in operations management. Journal Européen des Systèmes Automatisés, 2004, 38: 773–795 (in French)

52
Brownson J R S. Chapter 11–The Sun as Commons. In: Solar Energy Conversion Systems. Elsevier Inc., 2014, 287–305

DOI

53
Peffer T, Pritoni M, Meier A, Aragon C, Perry D. How people use thermostats in homes: a review. Building and Environment, 2011, 46(12): 2529–2541

DOI

54
Fideicomiso para el Ahorro de Energía Eléctrica. Educación para el Ahorroy Uso Racional de la Energía Eléctrica Educaree. 2016–09, available at fide.org.mx website

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