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A stochastic interior disturbance parameter model considering double uncertainty for probabilistic load calculation
https://orcid.org/0000-0001-6137-6747
Highlights A stochastic interior disturbance model considering double uncertainty is proposed. The fineness of the uncertainty quantification is improved. The impact of the absence of key uncertain factors on the results is analyzed.
Abstract The probabilistic method of calculating air-conditioning design load can effectively solve the oversizing problem in the deterministic method. However, the realization of the probabilistic method requires the quantification of the uncertain input parameters. Among them, the interior disturbance parameters are the key, whose uncertainties are caused by the unknown nature of interior configuration and the randomness of interior disturbance fluctuations. A stochastic interior disturbance parameter model considering double uncertainties (referred to simply as the DUSP model) was thus proposed. In this model, the uncertainty in the interior disturbance design parameters and interior disturbance time coefficients, which comes from two different sources, are described using probability distributions and occupant behavior models respectively. On this basis, the time series of interior disturbance parameters are generated by random sampling, to characterize the uncertainty of the interior disturbance parameters. Comparing the DUSP model with two models considering single-source uncertainty, the results showed that in the probabilistic load calculation, neglecting the uncertainty of the interior disturbance time coefficients may result in an overestimation of the design load by up to 9.2%; neglecting the uncertainty of the interior disturbance design parameters may result in an underestimation of the design load by up to 2.5%. Therefore, it is necessary to quantify the uncertainty in both sources simultaneously to ensure the reasonableness of the probabilistic load calculation.
A stochastic interior disturbance parameter model considering double uncertainty for probabilistic load calculation
https://orcid.org/0000-0001-6137-6747
Highlights A stochastic interior disturbance model considering double uncertainty is proposed. The fineness of the uncertainty quantification is improved. The impact of the absence of key uncertain factors on the results is analyzed.
Abstract The probabilistic method of calculating air-conditioning design load can effectively solve the oversizing problem in the deterministic method. However, the realization of the probabilistic method requires the quantification of the uncertain input parameters. Among them, the interior disturbance parameters are the key, whose uncertainties are caused by the unknown nature of interior configuration and the randomness of interior disturbance fluctuations. A stochastic interior disturbance parameter model considering double uncertainties (referred to simply as the DUSP model) was thus proposed. In this model, the uncertainty in the interior disturbance design parameters and interior disturbance time coefficients, which comes from two different sources, are described using probability distributions and occupant behavior models respectively. On this basis, the time series of interior disturbance parameters are generated by random sampling, to characterize the uncertainty of the interior disturbance parameters. Comparing the DUSP model with two models considering single-source uncertainty, the results showed that in the probabilistic load calculation, neglecting the uncertainty of the interior disturbance time coefficients may result in an overestimation of the design load by up to 9.2%; neglecting the uncertainty of the interior disturbance design parameters may result in an underestimation of the design load by up to 2.5%. Therefore, it is necessary to quantify the uncertainty in both sources simultaneously to ensure the reasonableness of the probabilistic load calculation.
A stochastic interior disturbance parameter model considering double uncertainty for probabilistic load calculation
https://orcid.org/0000-0001-6137-6747
Highlights A stochastic interior disturbance model considering double uncertainty is proposed. The fineness of the uncertainty quantification is improved. The impact of the absence of key uncertain factors on the results is analyzed.
Abstract The probabilistic method of calculating air-conditioning design load can effectively solve the oversizing problem in the deterministic method. However, the realization of the probabilistic method requires the quantification of the uncertain input parameters. Among them, the interior disturbance parameters are the key, whose uncertainties are caused by the unknown nature of interior configuration and the randomness of interior disturbance fluctuations. A stochastic interior disturbance parameter model considering double uncertainties (referred to simply as the DUSP model) was thus proposed. In this model, the uncertainty in the interior disturbance design parameters and interior disturbance time coefficients, which comes from two different sources, are described using probability distributions and occupant behavior models respectively. On this basis, the time series of interior disturbance parameters are generated by random sampling, to characterize the uncertainty of the interior disturbance parameters. Comparing the DUSP model with two models considering single-source uncertainty, the results showed that in the probabilistic load calculation, neglecting the uncertainty of the interior disturbance time coefficients may result in an overestimation of the design load by up to 9.2%; neglecting the uncertainty of the interior disturbance design parameters may result in an underestimation of the design load by up to 2.5%. Therefore, it is necessary to quantify the uncertainty in both sources simultaneously to ensure the reasonableness of the probabilistic load calculation.
A stochastic interior disturbance parameter model considering double uncertainty for probabilistic load calculation
Wu, Xia (author) / Niu, Jide (author) / Tian, Zhe (author) / Zhou, Ruoyu (author) / Hou, Xinyang (author)
Energy and Buildings ; 285
2023-02-07
Article (Journal)
Electronic Resource
English
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