HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique

Huang, Pei; Huang, Gongsheng; Wang, Yu

doi:10.1016/j.enbuild.2015.01.026

Cited by 105 publications

(41 citation statements)

References 19 publications

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“…validated by the developers (e.g., Type 666 for the chiller, Type 742 for the constant speed pump in the primary chilled water loop, Type 741 for the variable speed pump in the secondary chilled water loop, Type 744 for the ventilation fan), which are the same as those in previous studies[12,[67][68][69][70][71][72]. The energy consumption of the chiller is calculated from Equations 8-12, and the energy consumptions of the pumps and fans are estimated using Equations 13 and 14 respectively[71][72][73].…”

mentioning

confidence: 89%

“…The energy consumption of the chiller is calculated from Equations 8-12, and the energy consumptions of the pumps and fans are estimated using Equations 13 and 14 respectively[71][72][73]. The key air conditioning system in TRNSYS are summarized inTable 2, more detailed descriptions of the energy consumption evaluation of the air conditioning system are available in studies[67][68][69][70][71][72].…”

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confidence: 99%

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Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving

Cheng

Zhang

Huan

et al. 2019

Building and Environment

108

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Fo r m o r e info r m a tio n, in cl u di n g o u r p olicy a n d s u b mi s sio n p r o c e d u r e , pl e a s e c o n t a c t t h e R e p o si to ry Te a m a t: u si r@ s alfo r d. a c. u k .Please cite this article as: Cheng Y, Zhang S, Huan C, Oladokun MO, Lin Z, Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving, Building and Environment (2018), doi: https://doi. AbstractStratum ventilation can energy efficiently provide good inhaled indoor air quality with a proper operation (e.g., fresh outdoor air ratio). However, the non-uniform CO 2 distribution in a stratum-ventilated room challenges the provision of targeted indoor air quality. This study proposes an optimization on the fresh outdoor air ratio of stratum ventilation for both the targeted indoor air quality and maximal energy saving.A model of CO 2 concentration in the breathing zone is developed by coupling CO 2 removal efficiency in the breathing zone and mass conservation laws. With the developed model, the ventilation parameters corresponding to different fresh outdoor air ratios are quantified to achieve the targeted indoor air quality (i.e., targeted CO 2 concentration in the breathing zone). Using the fresh outdoor air ratios and corresponding ventilation parameters as inputs, energy performance evaluations of the air conditioning system are conducted by building energy simulations. The fresh outdoor air ratio with the minimal energy consumption is determined as the optimal

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confidence: 89%

mentioning

confidence: 99%

Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving

Cheng

Zhang

Huan

et al. 2019

Building and Environment

108

View full text Add to dashboard Cite

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“…Secondly, the renewable energy system is sized based on the under-estimated building energy generation and over-estimated building energy consumption. As a result, the renewable energy system is commonly oversized, with unnecessarily high initial investment and a low SCR [24,25]. On the other hand, the building energy generation and consumption are vulnerable to uncertainties associated with weather, building physical properties and etc.…”

Section: Introductionmentioning

confidence: 99%

“…The case studies consider 10,000 decision scenarios (i.e., users' preferences to the design criteria). The users' preferences in practice can significantly affect the overall performance of the finally selected design option [25]. Thus, different users' preferences should be taken into consideration to ensure the robustness of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty

et al. 2018

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Properly treating uncertainty is critical for robust system sizing of nearly/net zero energy buildings (ZEBs). To treat uncertainty, the conventional method conducts Monte Carlo simulations for thousands of possible design options, which inevitably leads to computation load that is heavy or even impossible to handle. In order to reduce the number of Monte Carlo simulations, this study proposes a response-surface-modelbased system sizing method. The response surface models of design criteria (i.e., the annual energy match ratio, self-consumption ratio and initial investment) are established based on Monte Carlo simulations for 29 specific design points which are determined by Box-Behnken design. With the response surface models, the overall performances (i.e., the weighted performance of the design criteria) of all design options (i.e., sizing combinations of photovoltaic, wind turbine and electric storage) are evaluated, and the design option with the maximal overall performance is finally

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“…Two group variables [14] that associated with building dynamic cooling load can be categorized as follows:…”

Section: Description Of Improved Arx Modelmentioning

confidence: 99%

An improved ARX model for hourly cooling load prediction of office buildings in different climates

2018

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Abstract. An attempt was made to develop an improved autoregressive with exogenous (ARX) model for office buildings cooling load prediction in five major climates of China. The cooling load prediction methods can be arranged into three categories: regression analysis, energy simulation, and artificial intelligence. Among them, the regression analysis methods using regression models are much simple and practical for real applications. However, traditional regression models are often helpless to manage multiparameter dynamic changes, making it not accurate as the other two categories. Many of the existing cooling load prediction studies use piecewise linearization to manage nonlinearity. To improve the prediction accuracy of regression analysis methods, higher order and interaction terms are included in improved ARX based on traditional ARX model. The improved ARX model consists of eight variables, with eleven coefficients accessed at a time. For applications and evaluations, an office building in major cities within each climatic zone was selected as a representation. These cities were Harbin, Beijing, Nanjing, Kunming and Guangzhou respectively. The coefficient of determination R 2 is greater than 0.9 in five cities. The prediction results show that the improved ARX model can adapt to different climatic conditions, including those nonlinearity cases.

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HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique

Cited by 105 publications

References 19 publications

Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving

Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving

Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty

An improved ARX model for hourly cooling load prediction of office buildings in different climates

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