Data Mining - Concepts and Techniques.

The adaptive hypothesis predicts that contextual factors and past thermal history modify building occupants' thermal expectations and preferences. One of the predictions of the adaptive hypothesis is that people in warm climate zones prefer warmer indoor temperatures than people living in cold climate zones. This is contrary to the static assumptions underlying the current ASHRAE comfort standard 55-92. To examine the adaptive hypothesis and its implications for Standard 55-92, the ASHRAE RP-884 project assembled a quality-controlled database from thermal comfort field experiments worldwide (circa 21,000 observations from 160 buildings). Our statistical analysis examined the semantics of thermal comfort in terms of thermal sensation, acceptability, and preference, as a function of both indoor and outdoor temperature. Optimum indoor temperatures tracked both prevailing indoor and outdoor temperatures, as predicted by the adaptive hypothesis. The static predicted means vote (PMV) model was shown to be partially adaptive by accounting for behavioral adjustments, and fully explained adaptation occurring in HVAC buildings. Occupants in naturally ventilated buildings were tolerant of a significantly wider range of temperatures, explained by a combination of both behavioral adjustment and psychological adaptation. These results formed the basis of a proposal for a variable indoor temperature standard.

/pdf/developing-an-adaptive-model-of-thermal-comfort-and-3tjjre7fl5.pdf

Developing an adaptive model of thermal comfort and preference - eScholarship

Phase change materials for thermal energy storage

Cooling the cities – A review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments

https://escholarship.org/content/qt5ts1r442/qt5ts1r442.pdf?t=lnq1j6

Thermal adaptation in the built environment: a literature review

Effectiveness of N95 Filters for Capturing Nano-Particles under cyclic and constant flow

Thermal Comfort Aspect of Three Different Heating Systems

The impact of building materials and their combinations on perceived indoor air quality

In order to quantify uncertainty in thermal building simulation stochastic modelling is applied on a building model. Part 1 deals with the stochastic thermal building model and a test case. This paper deals with the determination of the stochastic input loads. The importance of obtaining a proper statistical description of the input quantities to a stochastic model is addressed and exemplified by stochastic models for the external air temperature and the solar heat gain. Each of the external climate parameters is modelled as a stochastic process with time varying mean value function superimposed by a time varying standard deviation function. The statistics of the external air temperature is obtained by means of Fast Fourier Transform (FFT). A model of the solar heat gain is presented, considering the obvious fact that solar radiation is present only during daytime. The Danish Design Reference Year (DRY) is used as experimental data.

/pdf/quantification-on-uncertainty-in-thermal-building-simulation-1rxpwxd9i5.pdf

Quantification on Uncertainty in Thermal Building Simulation, Part 2: Stochastic Loads

A method for predicting long term performance of buildings is presented. The thermal performance of a building is sensitive to meteorological data which are stochastic in nature. A set of stochastic differential equations has been used to model the thermal behaviour of the building. The model can simultaneously consider randomness in the inputs and the coefficients of the model. Randomness in this case is modelled as a Gaussian white noise process. The moment equations are developed which provide the mean and variance of room air temperature and required heating or cooling load. Illustrative examples are presented.

System-theoretic approach of stochastic modelling of passive building thermal performance

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