Abstract
Optimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables optimal configuration of the system by opting for a suitable size factor. However, the representation of uncertainty in HVAC sizing has been limited to probabilistic analysis and scenario-based cases, which may limit and bias the results. This study provides a framework for uncertainty representation in building energy modeling, due to both random factors and imprecise knowledge. The framework is shown by a numerical case study of sizing cooling loads, in which uncertain climatic data are represented by probability distributions and human-driven activities are described by possibility distributions. Cooling loads obtained from the hybrid probabilistic–possibilistic propagation of uncertainty are compared to those obtained by pure probabilistic and pure possibilistic approaches. Results indicate that a pure possibilistic representation may not provide detailed information on the peak cooling loads, whereas a pure probabilistic approach may underestimate the effect of uncertain human behavior. The proposed hybrid representation and propagation of uncertainty in this paper can overcome these issues by proper handling of both random and limited data.
Issue Section:
Research Papers
Keywords:
Energy,
Environmental,
Evidence theory,
Fuzzy,
Probability,
Sustainability ,
Uncertainty,
Vague
References
1.
Birol
,
F.
, 2010
,
“World Energy Outlook 2010
,” Vol.
1
, International Energy Agency, Paris, France,
Report
.http://www.worldenergyoutlook.org/media/weo2010.pdf2.
Guillén-Lambea
,
S.
,
Rodríguez-Soria
,
B.
, and
Marín
, J.
M.
, 2016
, “Review of
European Ventilation Strategies to Meet the Cooling and Heating Demands of
Nearly Zero Energy Buildings (nZEB)/Passivhaus. Comparison With the
USA
,” Renewable Sustainable Energy Rev.
,
62
, pp. 561
–574
.3.
Allouhi
,
A.
, El
Fouih
, Y.
,
Kousksou
,
T.
,
Jamil
,
A.
,
Zeraouli
,
Y.
, and
Mourad
,
Y.
, 2015
,
“Energy Consumption and Efficiency in Buildings: Current
Status and Future Trends
,” J. Cleaner
Prod.
, 109
, pp.
118
–130
.4.
Pérez-Lombard
,
L.
,
Ortiz
,
J.
, and
Pout
,
C.
, 2008
,
“A Review on Buildings Energy Consumption
Information
,” Energy Build.
,
40
(3
), pp.
394
–398
.5.
Zhao
,
P.
,
Henze
, G.
P.
, Plamp
,
S.
, and
Cushing
, V.
J.
, 2013
,
“Evaluation of Commercial Building HVAC Systems as Frequency
Regulation Providers
,” Energy Build.
,
67
, pp. 225
–235
.6.
Vakiloroaya
,
V.
,
Samali
,
B.
,
Fakhar
,
A.
, and
Pishghadam
,
K.
, 2014
,
“A Review of Different Strategies for HVAC Energy
Saving
,” Energy Convers. Manage.
,
77
, pp. 738
–754
.7.
Wan Mohd
Nazi
, W. I.
,
Royapoor
,
M.
,
Wang
,
Y.
, and
Roskilly
, A.
P.
, 2017
,
“Office Building Cooling Load Reduction Using Thermal
Analysis Method—A Case Study
,” Appl.
Energy
, 185
(Pt. 2
), pp.
1574
–1584
.8.
Salari
,
E.
, and
Askarzadeh
,
A.
, 2015
,
“A New Solution for Loading Optimization of Multi-Chiller
Systems by General Algebraic Modeling System
,” Appl.
Therm. Eng.
, 84
, pp.
429
–436
.9.
Mitchell
,
J. W.
, and
Braun
, J.
E.
, 2013
, Principles of
Heating, Ventilation, and Air Conditioning in Buildings
,
Wiley
, Hoboken,
NJ
.10.
Schuetter
,
S.
,
DeBaillie
,
L.
, and
Ahl
,
D.
, 2014
,
“Future Climate Impacts on Building Design
,”
ASHRAE J.
, 56
(9
), pp.
36
–44
.http://www.calmac.com/stuff/contentmgr/files/0/ea4afe82e740c257680ac9e8d454ce18/pdf/future_climate_impacts_on_building_design.pdf11.
Djunaedy
,
E.
, Van
den Wymelenberg
, K.
,
Acker
,
B.
, and
Thimmana
,
H.
, 2011
,
“Oversizing of HVAC System: Signatures and
Penalties
,” Energy Build.
,
43
(2–3
), pp.
468
–475
.12.
Yik
,
F.
,
Lee
,
W.
,
Burnett
,
J.
, and
Jones
,
P.
, 1999
,
“Chiller Plant Sizing by Cooling Load Simulation as a Means
to Avoid Oversized Plant
,” HKIE Trans.
,
6
(2
), pp.
19
–25
.13.
Sun
,
Y.
,
Huang
,
P.
, and
Huang
,
G.
, 2015
,
“A Multi-Criteria System Design Optimization for Net Zero
Energy Buildings Under Uncertainties
,” Energy
Build.
, 97
, pp.
196
–204
.14.
Cheng
,
Q.
,
Wang
,
S.
, and
Yan
,
C.
, 2016
,
“Robust Optimal Design of Chilled Water Systems in Buildings
With Quantified Uncertainty and Reliability for Minimized Life-Cycle
Cost
,” Energy Build.
, 126
,
pp. 159
–169
.15.
Shen
,
L.
, and
Sun
,
Y.
, 2016
,
“Performance Comparisons of Two System Sizing Approaches for
Net Zero Energy Building Clusters Under Uncertainties
,”
Energy Build.
, 127
, pp.
10
–21
.16.
Gang
,
W.
,
Wang
,
S.
,
Xiao
,
F.
, and
Gao
,
D-C.
, 2015
,
“Robust Optimal Design of Building Cooling Systems
Considering Cooling Load Uncertainty and Equipment
Reliability
,” Appl. Energy
,
159
, pp. 265
–275
.17.
Burhenne
,
S.
,
Tsvetkova
,
O.
,
Jacob
,
D.
,
Henze
, G.
P.
, and Wagner
,
A.
, 2013
,
“Uncertainty Quantification for Combined Building Performance
and Cost-Benefit Analyses
,” Build.
Environ.
, 62
, pp.
143
–154
.18.
Yıldız
,
Y.
, and
Arsan
, Z.
D.
, 2011
,
“Identification of the Building Parameters That Influence
Heating and Cooling Energy Loads for Apartment Buildings in Hot-Humid
Climates
,” Energy
,
36
(7
), pp.
4287
–4296
.19.
Li
,
H.
, and
Wang
,
S.
, 2017
,
“Probabilistic Optimal Design Concerning Uncertainties and
On-Site Adaptive Commissioning of Air-Conditioning Water Pump Systems in
Buildings
,” Appl. Energy
,
202
, pp. 53
–65
.20.
Lee
,
K. H.
, and
Schiavon
,
S.
, 2014
,
“Influence of Three Dynamic Predictive Clothing Insulation
Models on Building Energy Use, HVAC Sizing and Thermal
Comfort
,” Energies
,
7
(4
), pp.
1917
–1934
.21.
Cheng
,
Q.
,
Wang
,
S.
,
Yan
,
C.
, and
Xiao
,
F.
, 2017
,
“Probabilistic Approach for Uncertainty-Based Optimal Design
of Chiller Plants in Buildings
,” Appl.
Energy
, 185
(Pt. 2
), pp.
1613
–1624
.22.
Mui
,
K.
, and
Wong
,
L.
, 2007
,
“Cooling Load Calculations in Subtropical
Climate
,” Build. Environ.
,
42
(7
), pp.
2498
–2504
.23.
Rasouli
,
M.
,
Ge
,
G.
,
Simonson
, C.
J.
, and
Besant
, R.
W.
, 2013
,
“Uncertainties in Energy and Economic Performance of HVAC
Systems and Energy Recovery Ventilators Due to Uncertainties in Building and
HVAC Parameters
,” Appl. Therm. Eng.
,
50
(1
), pp.
732
–742
.24.
Gang
,
W.
,
Wang
,
S.
,
Shan
,
K.
, and
Gao
,
D.
, 2015
,
“Impacts of Cooling Load Calculation Uncertainties on the
Design Optimization of Building Cooling Systems
,”
Energy Build.
, 94
, pp.
1
–9
.25.
Domínguez-Muñoz
,
F.
,
Cejudo-López
, J.
M.
, and
Carrillo-Andrés
,
A.
, 2010
,
“Uncertainty in Peak Cooling Load
Calculations
,” Energy Build.
,
42
(7
), pp.
1010
–1018
.26.
Sun
,
Y.
,
Gu
,
L.
,
Wu
, C.
J.
, and
Augenbroe
,
G.
, 2014
,
“Exploring HVAC System Sizing Under
Uncertainty
,” Energy Build.
,
81
, pp. 243
–252
.27.
Wang
,
Q.
,
Augenbroe
,
G.
,
Kim
,
J.-H.
, and
Gu
,
L.
, 2016
,
“Meta-Modeling of Occupancy Variables and Analysis of Their
Impact on Energy Outcomes of Office Buildings
,”
Appl. Energy
, 174
, pp.
166
–180
.28.
Corotis
,
R. B.
, 2015
,
“An Overview of Uncertainty Concepts Related to Mechanical
and Civil Engineering
,” ASCE-ASME J. Risk
Uncertainty Eng. Syst., Part B
,
1
(4
), p. 040801
.29.
Page
,
J.
,
Robinson
,
D.
,
Morel
,
N.
, and
Scartezzini
,
J.-L.
, 2008
,
“A Generalised Stochastic Model for the Simulation of
Occupant Presence
,” Energy Build.
,
40
(2
), pp.
83
–98
.30.
Richardson
,
I.
,
Thomson
,
M.
, and
Infield
,
D.
, 2008
,
“A High-Resolution Domestic Building Occupancy Model for
Energy Demand Simulations
,” Energy Build.
,
40
(8
), pp.
1560
–1566
.31.
Wang
,
C.
,
Yan
,
D.
, and
Jiang
,
Y.
, 2011, “A Novel
Approach for Building Occupancy Simulation
,” Build.
Simul.
, 4
(2
), pp.
149
–167
.32.
Tahmasebi
,
F.
, and
Mahdavi
,
A.
, 2015
,
“The Sensitivity of Building Performance Simulation Results
to the Choice of Occupants' Presence Models: A Case Study
,”
J. Build. Perform. Simul.
,
10
(5–6
), pp.
625
–635
.33.
D'Oca
,
S.
, and
Hong
,
T.
, 2015
,
“Occupancy Schedules Learning Process Through a Data Mining
Framework
,” Energy Build.
,
88
, pp. 395
–408
.34.
Miller
,
C.
,
Nagy
,
Z.
, and
Schlueter
,
A.
, 2015
,
“Automated Daily Pattern Filtering of Measured Building
Performance Data
,” Autom. Constr.
,
49
(Pt. A), pp. 1
–17
.35.
Eisenhower
,
B.
,
O'Neill
,
Z.
,
Fonoberov
, V.
A.
, and
Mezić
,
I.
, 2012
,
“Uncertainty and Sensitivity Decomposition of Building Energy
Models
,” J. Build. Perform. Simul.
,
5
(3
), pp.
171
–184
.36.
Azar
,
E.
, and
Amoodi
, A.
A.
, 2016
,
“Quantifying the Impact of Uncertainty in Human Actions on
the Energy Performance of Educational Buildings
,”
Winter Simulation Conference
, (WSC
),
Washington, DC, Dec. 11–14, pp. 1736
–1744
.37.
Kim
,
Y.-J.
, 2016
,
“Comparative Study of Surrogate Models for Uncertainty
Quantification of Building Energy Model: Gaussian Process Emulator Vs.
Polynomial Chaos Expansion
,” Energy Build.
,
133
, pp. 46
–58
.38.
Gaetani
,
I.
,
Hoes
,
P.-J.
, and
Hensen
, J.
L.
, 2016
, “Occupant
Behavior in Building Energy Simulation: Towards a Fit-for-Purpose Modeling
Strategy
,” Energy Build.
,
121
, pp. 188
–204
.39.
Dubois
,
D.
, and
Prade
,
H.
, 2009
,
“Formal Representations of Uncertainty
,”
Decision-Making Process: Concepts and Methods
, D. Bouyssou,
D. Dubios, M. Pirlot, and H. Parade, eds., Wiley, London, pp.
85
–156
.40.
Oberkampf
, W.
L.
, DeLand
,
S. M.
,
Rutherford
, B.
M.
, Diegert
,
K. V.
, and
Alvin
, K.
F.
, 2002
, “Error and
Uncertainty in Modeling and Simulation
,” Reliab.
Eng. Syst. Saf.
, 75
(3
), pp.
333
–357
.41.
Klir
,
G. J.
, 1994
,
“On the Alleged Superiority of Probabilistic Representation
of Uncertainty
,” IEEE Trans. Fuzzy Syst.
,
2
(1
), pp.
27
–31
.42.
Tian
,
W.
, 2013
,
“A Review of Sensitivity Analysis Methods in Building Energy
Analysis
,” Renewable Sustainable Energy
Rev.
, 20
, pp.
411
–419
.43.
D'Oca
,
S.
,
Hong
,
T.
, and
Langevin
,
J.
, 2018
,
“The Human Dimensions of Energy Use in Buildings: A
Review
,” Renewable Sustainable Energy Rev.
,
81
(Pt. 1), pp.
731
–742
.44.
Parsons
,
S.
, 1996
,
“Current Approaches to Handling Imperfect Information in Data
and Knowledge Bases
,” IEEE Trans. Knowl. Data
Eng.
, 8
(3
), pp.
353
–372
.45.
Guyonnet
,
D.
,
Bourgine
,
B.
,
Dubois
,
D.
,
Fargier
,
H.
,
Côme
,
B.
, and
Chilès
,
J.-P.
, 2003
,
“Hybrid Approach for Addressing Uncertainty in Risk
Assessments
,” J. Environ. Eng.
,
129
(1
), pp.
68
–78
.46.
Zio
,
E.
, 2013
,
The Monte Carlo Simulation Method for System Reliability and Risk
Analysis
, Springer
,
London
.47.
Lindley
,
D. V.
, 1987
,
“The Probability Approach to the Treatment of Uncertainty in
Artificial Intelligence and Expert Systems
,”
Statist. Sci.
, 2
(1
), pp.
17
–24
.http://www.jstor.org/stable/224560348.
Zadeh
,
L. A.
, 2008
,
“Is There a Need for Fuzzy Logic?
,”
Inf. Sci.
, 178
(13
), pp.
2751
–2779
.49.
Dubois
,
D.
, and
Prade
,
H.
, 2001
,
“Possibility Theory, Probability Theory and Multiple-Valued
Logics: A Clarification
,” Ann. Math. Artif.
Intell.
, 32
(1
), pp.
35
–66
.50.
Cobb
,
B. R.
, and
Shenoy
, P.
P.
, 2003
, “A
Comparison of Bayesian and Belief Function Reasoning
,”
Inf. Syst. Front.
, 5
(4
),
pp. 345
–358
.51.
Haenni
,
R.
, and
Lehmann
,
N.
, 2003
,
“Implementing Belief Function Computations
,”
Int. J. Intell. Syst.
,
18
(1
), pp.
31
–49
.52.
Dubois
,
D.
,
Prade
,
H.
, and
Smets
,
P.
, 1996
,
“Representing Partial Ignorance
,”
IEEE Trans. Syst. Man Cybern., Part A
,
26
(3
), pp.
361
–377
.53.
Kohlas
,
J.
, and
Monney
,
P.-A.
, 2013
, A
Mathematical Theory of Hints: An Approach to the Dempster-Shafer Theory of
Evidence
, Springer Science & Business
Media
,
Berlin
.54.
Chen
,
N.
,
Yu
,
D.
,
Xia
,
B.
, and
Beer
,
M.
, 2016
,
“Uncertainty Analysis of a Structural–Acoustic Problem Using
Imprecise Probabilities Based on p-Box Representations
,”
Mech. Syst. Signal Process.
, 80
, pp.
45
–57
.55.
Zhang
,
H.
,
Ha
,
L.
,
Li
,
Q.
, and
Beer
,
M.
, 2017
,
“Imprecise Probability Analysis of Steel Structures Subject
to Atmospheric Corrosion
,” Struct. Saf.
,
67
, pp. 62
–69
.56.
Rocchetta
,
R.
, and
Patelli
,
E.
, 2016
,
“Imprecise Probabilistic Framework for Power Grids Risk
Assessment and Sensitivity Analysis
,” Risk,
Reliability and Safety: Innovating Theory and Practice
,
CRC Press
,
London
, pp.
2789
–2796
.57.
Hopfe
,
C. J.
, and
Hensen
, J.
L.
, 2011
,
“Uncertainty Analysis in Building Performance Simulation for
Design Support
,” Energy Build.
,
43
(10
), pp.
2798
–2805
.58.
Heo
,
Y.
,
Choudhary
,
R.
, and
Augenbroe
,
G.
, 2012
,
“Calibration of Building Energy Models for Retrofit Analysis
Under Uncertainty
,” Energy Build.
,
47
, pp. 550
–560
.59.
Cooper
,
J. A.
,
Ferson
,
S.
, and
Ginzburg
,
L.
, 1996
,
“Hybrid Processing of Stochastic and Subjective Uncertainty
Data
,” Risk Anal.
,
16
(6
), pp.
785
–791
.60.
Baudrit
,
C.
,
Dubois
,
D.
, and
Perrot
,
N.
, 2008
,
“Representing Parametric Probabilistic Models Tainted With
Imprecision
,” Fuzzy Sets Syst.
,
159
(15
), pp.
1913
–1928
.61.
Zadeh
,
L. A.
, 1999
,
“Fuzzy Sets as a Basis for a Theory of
Possibility
,” Fuzzy Sets Syst.
,
100
(Suppl. 1), pp.
9
–34
.62.
Dubois
,
D.
,
Kerre
,
E.
,
Mesiar
,
R.
, and
Prade
,
H.
, 2000
,
“Fuzzy Interval Analysis
,”
Fundamentals of Fuzzy Sets
,
Springer
, New
York
, pp.
483
–581
.63.
Dubois
,
D.
, and
Prade
,
H.
, 1992
,
“When Upper Probabilities are Possibility
Measures
,” Fuzzy Sets Syst.
,
49
(1
), pp.
65
–74
.64.
Baraldi
,
P.
, and
Zio
,
E.
, 2008
,
“A Combined Monte Carlo and Possibilistic Approach to
Uncertainty Propagation in Event Tree Analysis
,”
Risk Anal.
, 28
(5
), pp.
1309
–1326
.65.
de Wilde
,
P.
, and
Tian
,
W.
, 2009
,
“Identification of Key Factors for Uncertainty in the
Prediction of the Thermal Performance of an Office Building Under Climate
Change
,” Build. Simul.
,
2
(3
), pp.
157
–174
.66.
Dubois
,
D.
,
Fargier
,
H.
, and
Fortin
,
J.
, 2005
,
“The Empirical Variance of a Set of Fuzzy
Intervals
,” 14th IEEE International Conference on
Fuzzy Systems
(FUZZ'05
), Reno, NV, May 22–25,
pp. 885
–890
.67.
Baudrit
,
C.
,
Dubois
,
D.
, and
Guyonnet
,
D.
, 2006
,
“Joint Propagation and Exploitation of Probabilistic and
Possibilistic Information in Risk Assessment
,” IEEE
Trans. Fuzzy Syst.
, 14
(5
), pp.
593
–608
.68.
Shafer
,
G.
, 1976
,
A Mathematical Theory of Evidence
,
Princeton University Press
,
Princeton, NJ
.69.
Mauris
,
G.
,
Berrah
,
L.
,
Foulloy
,
L.
, and
Haurat
,
A.
, 2000
,
“Fuzzy Handling of Measurement Errors in
Instrumentation
,” IEEE Trans. Instrum.
Meas.
, 49
(1
), pp.
89
–93
.70.
Masson
,
M.-H.
, and
Denœux
,
T.
, 2006
,
“Inferring a Possibility Distribution From Empirical
Data
,” Fuzzy Sets Syst.
,
157
(3
), pp.
319
–340
.71.
ASHRAE
, 2009
,
2009 ASHRAE® Handbook: Fundamentals, American Society of
Heating, Refrigerating and Air-Conditioning Engineers,
Inc
., Atlanta,
GA
.72.
Li
,
Y.
, and
Zio
,
E.
, 2012
,
“Uncertainty Analysis of the Adequacy Assessment Model of a
Distributed Generation System
,” Renewable
Energy
, 41
, pp.
235
–244
.73.
Sansavini
,
G.
,
Piccinelli
,
R.
,
Golea
,
L.
, and
Zio
,
E.
, 2014
,
“A Stochastic Framework for Uncertainty Analysis in Electric
Power Transmission Systems With Wind Generation
,”
Renewable Energy
, 64
, pp.
71
–81
.74.
Rocchetta
,
R.
,
Li
,
Y.
, and
Zio
,
E.
, 2015
,
“Risk Assessment and Risk-Cost Optimization of Distributed
Power Generation Systems Considering Extreme Weather
Conditions
,” Reliab. Eng. Syst. Saf.
,
136
, pp. 47
–61
.75.
Naseri
,
M.
,
Baraldi
,
P.
,
Compare
,
M.
, and
Zio
,
E.
, 2016
,
“Availability Assessment of Oil and Gas Processing Plants
Operating Under Dynamic Arctic Weather Conditions
,”
Reliab. Eng. Syst. Saf.
, 152
, pp.
66
–82
.76.
Tindale
,
A.
, 2005
,
“DesignBuilder Software
,”
Design-Builder Software Ltd
.,
Gloucestershire, UK
.77.
Trčka
,
M.
, and
Hensen
, J.
L.
, 2010
, “Overview
of HVAC System Simulation
,” Autom. Constr.
,
19
(2
), pp.
93
–99
.78.
Crawley
,
D. B.
,
Lawrie
, L.
K.
, Winkelmann
,
F. C.
,
Buhl
, W.
F.
, Huang
,
Y. J.
,
Pedersen
, C.
O.
, Strand
,
R. K.
,
Liesen
, R.
J.
, Fisher
,
D. E.
, and
Witte
, M.
J.
, 2001
,
“EnergyPlus: Creating a New-Generation Building Energy
Simulation Program
,” Energy Build.
,
33
(4
), pp.
319
–331
.79.
Zhang
,
Y.
, 2009
,
“‘Parallel’ EnergyPlus and the Development of a Parametric
Analysis Tool
,” 11th Conference of International
Building Performance Association
(IBPSA
),
Glasgow, UK, July 27–30, pp. 1382
–1388
.80.
Zhang
,
Y.
, and
Korolija
,
I.
, 2010
,
“Performing Complex Parametric Simulations With
jEPlus
,” Ninth International Conference on
Sustainable Energy Technologies
(SET
),
Shanghai, China, Aug. 24–27.81.
Zhang
,
Y.
, 2012
,
“Use jEPlus as an Efficient Building Design Optimisation
Tool
,” CIBSE ASHRAE Technical Symposium
,
London, Apr. 18–19, pp. 18
–19
.82.
Das
,
P.
,
Shrubsole
,
C.
,
Jones
,
B.
,
Hamilton
,
I.
,
Chalabi
,
Z.
,
Davies
,
M.
,
Mavrogianni
,
A.
, and
Taylor
,
J.
, 2014
,
“Using Probabilistic Sampling-Based Sensitivity Analyses for
Indoor Air Quality Modelling
,” Build.
Environ.
, 78
, pp.
171
–182
.83.
Zahiri
,
B.
,
Tavakkoli-Moghaddam
,
R.
, and
Pishvaee
, M.
S.
, 2014
, “A
Robust Possibilistic Programming Approach to Multi-Period
Location–Allocation of Organ Transplant Centers Under
Uncertainty
,” Comput. Ind. Eng.
,
74
, pp. 139
–148
.84.
Singh
,
M.
, and
Markeset
,
T.
, 2014
,
“Hybrid Models for Handling Variability and Uncertainty in
Probabilistic and Possibilistic Failure Analysis of Corroded
Pipes
,” Eng. Failure Anal.
,
42
, pp. 197
–209
.85.
Thevenard
,
D.
, 2010
,
“Influence of Long-Term Trends and Period of Record Selection
on the Calculation of Climatic Design Conditions and Degree
Days
,” ASHRAE Trans.
,
116
(1
), pp.
447
–460
.86.
Kandya
,
A.
, and
Mohan
,
M.
, 2018
,
“Mitigating the Urban Heat Island Effect Through Building
Envelope Modifications
,” Energy Build.
,
164
, pp. 266
–277
.87.
Paolini
,
R.
,
Zani
,
A.
,
MeshkinKiya
,
M.
,
Castaldo
, V.
L.
, Pisello
,
A. L.
,
Antretter
,
F.
,
Poli
,
T.
, and
Cotana
,
F.
, 2017
,
“The Hygrothermal Performance of Residential Buildings at
Urban and Rural Sites: Sensible and Latent Energy Loads and Indoor
Environmental Conditions
,” Energy Build.
,
152
(Suppl. C
), pp.
792
–803
.88.
Lombardia
,
A.
, 2006
,
Rapporto sullo stato dell'ambiente in Lombardia
,
Regione Lombardia
, Milan,
Italy
.89.
Thevenard
,
D.
, and
Cornick
,
S.
, 2013
,
“Revising ASHRAE Climatic Data for Design and Standards—Part
1: Overview and Data
,” ASHRAE Trans.
,
119
(2
), pp.
181
–193
.90.
Duarte
,
C.
, Van
Den Wymelenberg
, K.
,
and Rieger
,
C.
, 2013
,
“Revealing Occupancy Patterns in an Office Building Through
the Use of Occupancy Sensor Data
,” Energy
Build.
, 67
, pp.
587
–595
.91.
Huang
,
Y. J.
, 2014
,
“An Evaluation of ASHRAE's Climatic Design Conditions Against
Actual Long-Term Recorded Weather Data
,” ASHRAE
Trans.
, 120
, pp.
V1
–V8
.92.
Helton
,
J. C.
, and
Davis
, F.
J.
, 2003
, “Latin
Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex
Systems
,” Reliab. Eng. Syst. Saf.
,
81
(1
), pp.
23
–69
.93.
Ruparathna
,
R.
,
Hewage
,
K.
, and
Sadiq
,
R.
, 2017
,
“Economic Evaluation of Building Energy Retrofits: A Fuzzy
Based Approach
,” Energy Build.
,
139
, pp. 395
–406
.94.
Sunitha
,
K.
, and
Behera
,
S.
, 2016
,
“Comparison of Conventional Control Techniques for an Energy
Efficient HVAC Systems
,” Int. J. Appl. Eng.
Res.
, 11
(6
), pp.
4258
–4263
.http://www.ripublication.com/ijaer16/ijaerv11n6_87.pdf95.
Keshtkar
,
A.
,
Arzanpour
,
S.
,
Keshtkar
,
F.
, and
Ahmadi
,
P.
, 2015
,
“Smart Residential Load Reduction Via Fuzzy Logic, Wireless
Sensors, and Smart Grid Incentives
,” Energy
Build.
, 104
, pp.
165
–180
.96.
Dubois
,
D.
,
Prade
,
H.
, and
Sandri
,
S.
, 1993
,
“On Possibility/Probability Transformations
,”
Fuzzy Logic
, Springer
,
Dordrecht, The Netherlands, pp.
103
–112
.97.
Dubois
,
D.
,
Foulloy
,
L.
,
Mauris
,
G.
, and
Prade
,
H.
, 2004
,
“Probability-Possibility Transformations, Triangular Fuzzy
Sets, and Probabilistic Inequalities
,” Reliable
Comput.
, 10
(4
), pp.
273
–297
.98.
Dubois
,
D.
,
Prade
,
H.
, and
Smets
,
P.
, 2008
,
“A Definition of Subjective Possibility
,”
Int. J. Approximate Reasoning
,
48
(2
), pp.
352
–364
.99.
Flage
,
R.
,
Baraldi
,
P.
,
Zio
,
E.
, and
Aven
,
T.
, 2013
,
“Probability and Possibility-Based Representations of
Uncertainty in Fault Tree Analysis
,” Risk
Anal.
, 33
(1
), pp.
121
–133
.100.
Mouchaweh
, M.
S.
, Bouguelid
,
M. S.
,
Billaudel
,
P.
, and
Riera
,
B.
, 2006
,
“Variable Probability-Possibility
Transformation
,” 25th European Annual Conference on Human
Decision Making and Manual Control (EAM'06
), Valenciennes,
France, Sept. 26–29.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.95.5933&rep=rep1&type=pdf101.
Pedroni
,
N.
,
Zio
,
E.
,
Pasanisi
,
A.
, and
Couplet
,
M.
, 2015
,
“Empirical Comparison of Two Methods for the Bayesian Update
of the Parameters of Probability Distributions in a Two-Level Hybrid
Probabilistic-Possibilistic Uncertainty Framework for Risk
Assessment
,” ASCE-ASME J. Risk Uncertainty Eng.
Syst., Part A
, 2
(1
), p.
04015015
.Copyright © 2018 by ASME
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