Neural network models in greenhouse air temperature prediction

Ferreira, Pedro; Faria, Eugénio de Araújo; Ruano, A. E.

doi:10.1016/s0925-2312(01)00620-8

Cited by 139 publications

(58 citation statements)

References 19 publications

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“…The training procedure progresses iteratively using the LM algorithm minimising criterion (13), until a termination criterion is satisfied. For more details about the training algorithm and the training criterion the reading of [40,[42][43][44] is recommended.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Neural networks based predictive control for thermal comfort and energy savings in public buildings

Ferreira¹,

Ruano²,

Silva³

et al. 2012

Energy and Buildings

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370

206

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a b s t r a c tThe paper addresses the problem of controlling a Heating Ventilation and Air Conditioning (HVAC) system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most conditions are conflicting goals requiring an optimisation method to find appropriate solutions over time. A discrete model-based predictive control methodology is applied, consisting of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and maintain thermal comfort; and the optimisation method, a discrete branch and bound approach. Each component will be described, with special emphasis on a fast and accurate computation of the PMV indices. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer and winter conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.

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Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Neural networks based predictive control for thermal comfort and energy savings in public buildings

Ferreira¹,

Ruano²,

Silva³

et al. 2012

Energy and Buildings

Self Cite

370

206

View full text Add to dashboard Cite

show abstract

“…Thus, estimating the amount of STE is inevitably necessary when deciding on the installation of the STE utilization system. Many studies on greenhouse thermal modeling have been conducted considering environmental control systems (Sharma et al, 1999;Chou et al, 2004;Hepbasli, 2011), thermal curtains and earth-air heat exchangers (Shukla et al, 2006), neural network or computer-aided modeling (Ferreira et al, 2002;Han et al, 2009), the effect of greenhouse orientation (Sethi, 2009), solar energy (Hamdan et al, 1992;Abdel-Ghany & Al-Helal, 2011), and thermal storages (Gauthier et al, 1997;Najjar & Hasan, 2008;Lee et al, 2011;Vadiee & Martin, 2012). In this study, a thermal model for the greenhouse was adopted into a test greenhouse including an STE utilization system.…”

Section: Al 2009mentioning

confidence: 99%

Surplus thermal energy model of greenhouses and coefficient analysis for effective utilization

et al. 2016

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This study concentrates on the utilization of surplus thermal energy (STE) in greenhouses. Even in the cold season, the thermal energy input from solar radiation easily exceeds the required amount for heating on a clear day in the temperate or subtropical regions. This excessive solar energy is regarded as STE. There are several studies related to the STE energy (Pavlou, 1991; Suh et RESEARCH ARTICLE OPEN ACCESS AbstractIf a greenhouse in the temperate and subtropical regions is maintained in a closed condition, the indoor temperature commonly exceeds that required for optimal plant growth, even in the cold season. This study considered this excess energy as surplus thermal energy (STE), which can be recovered, stored and used when heating is necessary. To use the STE economically and effectively, the amount of STE must be estimated before designing a utilization system. Therefore, this study proposed an STE model using energy balance equations for the three steps of the STE generation process. The coefficients in the model were determined by the results of previous research and experiments using the test greenhouse. The proposed STE model produced monthly errors of 17.9%, 10.4% and 7.4% for December, January and February, respectively. Furthermore, the effects of the coefficients on the model accuracy were revealed by the estimation error assessment and linear regression analysis through fixing dynamic coefficients. A sensitivity analysis of the model coefficients indicated that the coefficients have to be determined carefully. This study also provides effective ways to increase the amount of STE.Additional key words: energy balance; energy conservation; environmental control; heat pump; heat storage Abbreviations used: FCU (fan coil unit); HST (high temperature heat storage tank); LST (low temperature heat storage tank); mPLAI (modified PLAI, ratio of total projected leaf area to the floor area); PLAI (projected leaf area index, projected area of horizontal leaves per unit of horizontal land below); SHEF (sensible heat emission factor); STE (surplus thermal energy) Citation: Yang, S. H.; Son, J. E.; Lee, S. D.; Cho, S. I.; Ashtiani-Araghi, A.; Rhee, J. Y. (2016). Surplus thermal energy model of greenhouses and coefficient analysis for effective utilization.

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“…In the factory building, they used average indoor temperature, total internal heat power, temperature of water flowing in pipes behind one wall, temperature of water flowing in pipes behind the other wall and ventilation flow rate as inputs. Other works can be found in Mechaqrane and Zouak (2003), Ferreira and Ruano (2001), Gouda et al (2002), Frausto and Peters (2004) and Ferreira et al (2002).…”

Section: Predictive Modelingmentioning

confidence: 99%

A novel methodology for estimating space air change rates and occupant CO2 generation rates from measurements in mechanically-ventilated buildings

Lu¹,

Knuutila²,

Viljanen³

et al. 2010

Building and Environment

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Neural network models in greenhouse air temperature prediction

Cited by 139 publications

References 19 publications

Neural networks based predictive control for thermal comfort and energy savings in public buildings

Neural networks based predictive control for thermal comfort and energy savings in public buildings

Surplus thermal energy model of greenhouses and coefficient analysis for effective utilization

A novel methodology for estimating space air change rates and occupant CO2 generation rates from measurements in mechanically-ventilated buildings

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