The aim of the project presented in this article was to design a system which allows for assessment of indoor environmental conditions, with a special consideration regarding the comfort of occupants. The system consists of two microprocessor devices with numerous sensors, as well as a PC application which includes fuzzy inference module. Fuzzy inference algorithm allows for comfort assessment based on data gathered by sensors. It can also help to analyse the efficiency of HVAC systems. The article includes description of the system's functions and selection criteria for sensors taking into account of measurands. Also described in this paper is the construction of knowledge base, based on information from environmental standards and experts' statements. The constructed system was tested and examined, to confirm its application in practical comfort assessment and to highlight the advantages of using fuzzy logic in the process of analysing measured parameters and inference, which captures the way living beings process data.
TomažičSLogarVKristlŽKrainerAŠkrjancIKoširM. Indoor-environment simulator for control design purposes. Build Environ2013; 70: 60–72.
2.
MoonJWLeeJ-HKimS. Influence of control logic on variation of indoor thermal environment for residential buildings. Indoor Built Environ2015; 25: 916–933.
3.
ZhuQYiJShengSWenCHuH. A computer-aided modeling and measurement system for environmental thermal comfort sensing. IEEE Trans Intrumentation Meas2014; 64: 1–9.
4.
Yifan L, Ning L and Shaoyuan L. ANFIS modeling of the PMV thermal comfort index based on prior knowledge. In: 2014 IEEE 9th conference on industrial electronics and applications (ICIEA), Hangzhou, China; IEEEXplore, June 9–11 2014; pp. 214–219.
5.
Fang B, Xu Q, Park T and Zhang M. AirSense: an intelligent home-based sensing system for indoor air quality analytics. In: Proceedings of the 2016 ACM International joint conference on pervasive and ubiquitous computing, Heidelberg, Germany, September 12–16 2016; pp. 109–119.
6.
De GiuliVZecchinRCorainLSalmasoL. Measurements of indoor environmental conditions in Italian classrooms and their impact on children's comfort. Indoor Built Environ2014; 24: 689–712.
7.
FredaDCaponePdel GaudioMLenzuniP. A unified method to compute limits of acceptability for thermal comfort and indoor air quality at the workplace. Indoor Built Environ2017; 26: 199–207.
8.
MaurisGBenoitEFoulloyL. Fuzzy symbolic sensors – from concept to applications. Measurement1994; 12: 357–384.
9.
MaurisGBenoitEFoulloyL. The aggregation of complementary information via fuzzy sensors. Meas J Int Meas Confed1996; 17: 235–249.
10.
DounisAISantamourisMJLefasCC. Building visual comfort control with fuzzy reasoning. Energy Convers Manage1993; 34: 17–28.
11.
DounisAIBruantMSantamourisMGuarracinoGMichelP. Comparison of conventional and fuzzy control of indoor air quality in buildings. J Intell Fuzzy Syst1996; 4: 131–140.
12.
DounisAIManolakisDE. Design of a fuzzy system for living space thermal-comfort regulation. Appl Energy2001; 69: 119–144.
13.
BataynehWAl-AraidahOBatainehK. Fuzzy logic approach to provide safe and comfortable indoor environment. Int J Eng Sci2010; 2: 65–72.
14.
GrychowskiT. Hazard assessment based on fuzzy logic. Arch Min Sci2008; 53: 595–602.
15.
GrychowskiT. Multi sensor fire hazard monitoring in underground coal mine based on fuzzy inference system. J Intell Fuzzy Syst2014; 26: 345–351.
16.
FrontczakMWargockiP. Literature survey on how different factors influence human comfort in indoor environments. Build Environ2011; 46: 922–937.
17.
ANSI/ASHRAE. Standard 55:2013 Thermal environmental conditions for human occupancy (ANSI approved), Atlanta: ASHRAE, 2013.
18.
FangerPO. Thermal comfort – analysis and applications in environmental engineering, Copenhagen: Danish Technical Press, 1970.
19.
ISO 7730. Ergonomics of the thermal environment – analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, Geneva: International Organization for Standardization, 2005.
20.
HillRJSmithPA. Exposure Assessment for carbon dioxide gas: full shift average and short-term measurement approaches. J Occup Environ Hyg2015; 12: 819–828.
21.
RamalhoOWyartGMandinCBlondeauPCabanesP-ALeclercNMellotJUBoulangerGRedaelliM. Association of carbon dioxide with indoor air pollutants and exceedance of health guideline values. Build Environ2015; 93: 115–124.
EN 12665:2011 Light and lighting. Basic terms and criteria for specifying lighting requirements, Brussels: CEN, 2011.
24.
BS EN 12464-1:2011 Light and lighting. Lighting of work places. Indoor work places, London: BSI, 2011.
25.
DuffyJFCzeislerCA. Effect of light on human circadian physiology. Sleep Med Clin2009; 4: 165–177.
26.
MartellottaFSimoneADella CrociataSD'AlbaM. Global comfort and indoor environment quality attributes for workers of a hypermarket in Southern Italy. Build Environ2015; 95: 355–364.
27.
BerglundBThomasLDietrichHS. Guidelines for community noise. The WHO Expert task force meeting on guidelines for community noise, Geneva, April 1999, pp. 26–30. .
28.
ZadehLA. Fuzzy sets. Inf Control1965; 8: 338–353.
29.
ZadehLA. From computing with numbers to computing with words. From manipulation of measurements to manipulation of perceptions. IEEE Trans Circuits Syst – I Fundam Theory Appl1999; 45: 105–119.
30.
FoulloyLGalichetS. Fuzzy sensors for fuzzy control. Int J Uncertainty Fuzziness Knowledge-Based Syst1994; 2: 55–66.
31.
HossainANazirMRameshSRahmanAWongKH. Fuzzy logic-based model for prediction of building wall humidity level. Indoor Built Environ2014; 23: 565–573.
32.
ZadehLA. The concept of a linguistic variable and its application to approximate reasoning – I. Inf Sci (Ny)1975; 8: 199–249.
33.
MamdaniEH. Application of fuzzy algorithms for control of simple dynamic plant. Proc Inst Electr Eng1974; 121: 1585.
34.
Rozporządzenie Ministra Pracy i Polityki Socjalnej z dnia 26 września 1997 r. w sprawie ogólnych przepisów bezpieczeństwa i higieny pracy. Warsaw: Polish Minister of Labour and Social Policy, 1997.
35.
PN-B-03421:1978 Wentylacja i klimatyzacja – parametry obliczeniowe powietrza wewnętrznego w pomieszczeniach przeznaczonych do stałego przebywania ludzi. PKN, 1978.
36.
EN 15251:2012. Indoor environmental input parameters for design and assessment of energy performance of buildings – addressing indoor air quality, thermal environment, lighting and acoustics, Brussels: CEN, 2012.
37.
Directive 2002/91/EC of European Parliament and the Council of 16 December 2002 on the Energy Performance of the Buildings. European Commission, Brussels, 2002.
38.
ANSI/ASHRAE. Standard 62.1-2016 ventilation for acceptable indoor air quality, Atlanta: ASHRAE, 2016.
39.
WyonDPSandbergM. Discomfort due to vertical thermal gradients. Indoor Air1996; 6: 48–54.
40.
Office of Radiation and Indoor Air. EPA assessment of risks from radon in homes, Washington: US Environmental Protection Agency, 2003.
41.
Polednik B, Kozak K, Mazur J, Dudzińska M, Grzadziel D and Polednik A. Radon equilibrium factor and perceived air quality in an air-conditioned auditorium in Lublin, Poland. Indoor Built Environ. 2018; 27: 270–277.
42.
KolokotsaD. Comparison of the performance of fuzzy controllers for the management of the indoor environment. Build Environ2003; 38: 1439–1450.
43.
Chen W-K. Study on fuzzy determination of indoor environmental quality in the intelligent building. In: ICMLC 2013: international conference on machine learning and cybernetics, Tianjin, China, July 14–17 2013; pp. 1285–1290.
44.
Molina-Solana M, Ros M and Delgado M. Unifying fuzzy controller for indoor environment quality. In: 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), Edmond, Canada, June 24–28 2013; pp. 1080–1085.
45.
Wu C-H, Ma L-S, Chen C-H and Liu Y-W. A design of fuzzy controller for conforming to the regulations of indoor air quality and thermal comfort. In: CYBER 2014: IEEE 4th Annual international conference on cyber technology in Automation, control, and intelligent systems, Hong Kong, July 4–7 2014; pp. 383–388.
46.
CzogalaELeskiJ. Fuzzy and neuro-fuzzy intelligent systems, Heidelberg: Physica, 2000.
