Original papers
Sensitivity analysis of CSGHEAT model for estimation of heating consumption in a Chinese-style solar greenhouse

https://doi.org/10.1016/j.compag.2018.08.040Get rights and content

Highlights

  • One-parameter-at-a-time (OAT) approach was used for the sensitivity analysis.

  • Default values of the CSHHEAT model are reasonably acceptable.

  • Heating demand is highly sensitive to the thermal properties of cover, thermal blanket, and greenhouse perimeter.

  • Small change of indoor temperature and RH have significant effects on the heating demand for the coldest months.

Abstract

The sensitivity of a heating simulation model (CSGHEAT) was performed for estimation of the time-dependent heating requirement in a Chinese-style solar greenhouse in cold region. Results showed that the constant value of air thermal conductance is the main default parameter of the model that significantly affected the model output. The results also indicated the heating requirement is highly sensitive to the greenhouse design parameters including the thermal properties of cover, thermal blanket, and greenhouse perimeter. The thermal blanket is the most important design parameter for the Chinese-style solar greenhouse, and the heating requirement could be increased between 32 and 41% during the coldest three months (January, February, and December) for changing the thermal conductivity from 0.01 to 0.05 W/m K. Increasing daytime indoor set-point temperature from 19 to 23 °C would increase the heating demand between 13 and 20%, whereas the heating demand could be increased by 9–18% for increasing the night-time temperature from 16 to 20 °C. Results also indicate the heating demand could be reduced up to 20% during the coldest period for increasing the indoor relative humidity from 70 to 90%. The results from this study could be useful for understanding the energy saving management of greenhouse operations and for designing the energy-efficient Chinese-style solar greenhouses in cold regions.

Introduction

Chinese-style Solar Greenhouses (CSGs) have become popular to grow vegetables without any auxiliary heating or with minimum heating depending on the locations of greenhouses. CSGs are mostly used in China and are also being adopted by many countries including Canada. The adaptation of the CSGs beyond China might require some modification in design and environmental control systems. In northern China (32–43°N), mostly no auxiliary heating is supplied to the greenhouse (Tong et al., 2009), but supplemental heating might be required for extending the growing period in relatively high northern latitudes. The heating requirement in a typical CSG located in Saskatoon (52.13°N) could be about 50% less as compared to a typical gutter connected commercial greenhouse (Ahamed et al., 2016). However, a substantial amount of supplemental heating is still required for year-round production at high northern latitudes.

A few thermal models (Guo et al., 1994, Ma et al., 2010, Meng et al., 2009) have been developed to simulate the microclimates of CSGs. However, almost all of the models are developed for simulation of temperature variation of different components in the CSGs. Ahamed et al. (2018) developed and validated a time-dependent heating simulation model (CSGHEAT) for estimation of the heating requirement in the CSGs. Greenhouse thermal models are usually developed based on some assumptions and approximation of different heat transfer parameters. It is important to analyze the effect of these parameters on the model output with a different value before the developed model is incorporated into a practical application. Also, the variation of some user-defined input variables about greenhouse design and indoor environmental control systems could greatly affect the model output. As some variables have a higher impact than others on heating needs, the identification of highly sensitive variables is important from both a technical and economic perspective and should be handled with utmost care (Lam and Hui, 1996). Sensitivity analysis could identify the most influential parameters of the greenhouse on its overall performance such as heating or cooling demand of greenhouses. It can also be used to assess the set of parameters which has the greatest influence on the building performance and the degree of influence. Accordingly, in order to get the accurate prediction from the heating simulation tool such as CSGHEAT, it is important to understand its sensitivity to the input parameters and building envelope materials, and environmental control parameters.

Several studies can be found in literature about sensitivity analysis performed for greenhouse simulation models to evaluate the effects of technical parameters and physical parameters on the output (Chalabi and Bailey, 1991, Navas et al., 1997, Van Henten, 2003, Vanthoor et al., 2011). Chalabi and Bailey (1991) evaluated the sensitivity of a non-steady state energy and moisture balance model to its parameters for the conventional greenhouses. Van Henten (2003) studied the sensitivity of the greenhouse climate control model to evaluate the impact of the model parameters on the performance. However, the sensitivity of greenhouse heating simulation models has rarely been studied, and moreover, most of sensitivity studies for greenhouse thermal models have involved the conventional-style greenhouses, not the Chinese-style solar greenhouses, which was the main focus of this study.

The objective of this study was to conduct a sensitivity analysis of a recently developed heating simulation model (CSGHEAT) for estimation of the heating energy requirement in a CSG at high northern latitudes. The results could be helpful to understand the degrees of sensitivity of the model to various influential parameters, and also to better understand the energy-efficient design principles and operating strategies of environmental control systems used in cold regions.

Section snippets

Heating simulation model (CSGHEAT)

The CSGHEAT model was developed for simulation of time-dependent heating requirements in the CSGs, and the cooling load was not considered because the greenhouse temperature is usually controlled by opening the vent near the ridge. The model was developed based on the heat balance of indoor greenhouse air, and the heat sources and sinks of the greenhouse were estimated based on the lumped estimation methods. The general heat balance equation of the developed model is given by Ahamed et al. (2018

Sensitivity of CSGHEAT model to default parameters

The thermal model was developed based on some assumptions to reduce the complexity of the model. The sensitivity of the model to some important parameters with default values related to the assumptions in model development was conducted to evaluate their significance on the model output. These parameters include air thermal conductance of double-layer cover, greenhouse floor parameters, and characteristic length of convective surfaces.

Conclusions and recommendations

In this study, the sensitivity of the heating simulation model (CSGHEAT) was conducted to evaluate the performance of the model for different values of the selected default parameters and also the sensitivity of design parameters and environmental control parameters on the heating requirement. The results indicate that the value used for default parameters in model development is reasonably acceptable. The sensitivity analysis also indicates the design parameters including the thermal

Acknowledgment

The authors are highly thankful to the College of Graduate and Postdoctoral Studies (CGPS) at the University of Saskatchewan, and Innovation Saskatchewan for their financial support to the research.

References (40)

  • J. Wang et al.

    Simulation and optimization of solar greenhouses in Northern Jiangsu Province of China

    Energy Build.

    (2014)
  • Ahamed, M.S., Guo, H., Tanino, K.K., 2016. Modeling of heating requirement in Chinese Solar Greenhouse. In: 2016...
  • Aldrich, R.A., Bartok, J.W., 1994. Greenhouse Enginering. In: Hill (Ed.), Dynamic modeling of tree growth and energy...
  • ASABE, 2006. Heating, ventilating, and cooling greenhouse. ASABE standards, 53rd ed. ASABE, St. Joseph Charter...
  • ASHRAE, 2013. ASHRAE Handbook of Fundamentals, SI ed. American Society of Heating Ventilation Refrigeration and...
  • J.C. Bakker

    Analysis of Humidity Effects on Growth and Production of Glasshouse Fruit Vegetables

    (1991)
  • E. Beshada et al.

    Winter performance of a solar energy greenhouse in southern Manitoba

    Can. Biosyst. Eng.

    (2006)
  • Buschermohle, M.J., Grandle, G.F., 2002. Controlling the environment in greenhouses used for tomato production....
  • N. Castilla

    Greenhouse Technology and Management

    (2013)
  • Dorais, M., 2003. The use of supplemental lighting for vegetable crop production: light intensity, crop response,...
  • Cited by (18)

    • Sensitivity analysis of the DehumReq model to evaluate the impact of predominant factors on dehumidification requirement of greenhouses in cold regions

      2023, Information Processing in Agriculture
      Citation Excerpt :

      In greenhouse temperature control, Chalabi et al. [6] found the transparent cover to ground area ratio is the most sensitive parameter followed by LAI, solar absorptivity of internal objects, heat transfer coefficient between cover and air, and infiltration rate [6]. For Chinese style solar greenhouse, Ahamed et al. [9] reported the transmissive properties of greenhouse cover, thermal blanket, and insulation capacity of cover as the most sensitive parameters for temperature control. None of these sensitivity analyses considered the changeable technical parameters (air exchange, indoor setpoints, etc.).

    • Bioeconomic evaluation of extended season and year-round tomato production in Norway using supplemental light

      2022, Agricultural Systems
      Citation Excerpt :

      We carried out a local sensitivity analysis (LSA) (Tian, 2013) in order to analyse the effect of tomato prices on the NFR. Since the LSA does not take into account the relationship between the various input variables, we also carried out global sensitivity analysis (GSA) (Tian, 2013; Ahamed et al., 2018) by simultaneously varying the electricity, natural gas and tomato prices. To be precise, we varied the electricity and natural gas prices from 0.3 NOK kWh−1 to 0.65 NOK kWh−1, with a step size of 0.05 NOK kWh−1 and the tomato prices from 14 NOK kg−1 to 21 NOK kg−1, with a step size of 1 NOK kg−1.

    View all citing articles on Scopus
    View full text