Modelling the Occupancy Profile Deterministically, Probabilistically and Stochastically
Akhondzada, Ali (2017)
Metropolia Ammattikorkeakoulu
2017
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2017053011130
https://urn.fi/URN:NBN:fi:amk-2017053011130
Tiivistelmä
This bachelor’s thesis aimed at analyzing and comparing the properties and principles of deterministic, probabilistic and stochastic occupancy approaches using various models. For each model, the results of this analytical assessment were compared against either the outcomes of similar models or the results of measured databases. The purpose was to evaluate the performance of the different occupancy models.
Literary sources, such as articles and books were reviewed. For the assessment of the functionality of the models functionality, algorithms presented in each model were followed step by step and their formulas and equation sets tested with random numbers.
The final results showed that the models based on probabilistic and stochastic approaches could simulate the occupancy rate more accurately compared to the models based on the deterministic approaches. The results of the methodological review also showed that each model can capture a certain number of diversity factors of occupancy rate. The findings from this thesis can be used for developing a new occupancy model based on a combination of both stochastic and probabilistic approaches. Furthermore, the developed model can be integrated in developing a building simulation tool.
Literary sources, such as articles and books were reviewed. For the assessment of the functionality of the models functionality, algorithms presented in each model were followed step by step and their formulas and equation sets tested with random numbers.
The final results showed that the models based on probabilistic and stochastic approaches could simulate the occupancy rate more accurately compared to the models based on the deterministic approaches. The results of the methodological review also showed that each model can capture a certain number of diversity factors of occupancy rate. The findings from this thesis can be used for developing a new occupancy model based on a combination of both stochastic and probabilistic approaches. Furthermore, the developed model can be integrated in developing a building simulation tool.