Controls in Solar Energy Systems

Winn, C. B.

doi:10.1007/978-1-4684-8992-7_8

Cited by 14 publications

(20 citation statements)

References 8 publications

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“…At low and intermediate temperatures, the heat losses of the solar collector are dominated by the convection and/or conduction and the overall heat loss coefficient U L may be used so as to include the radiation losses in the linearized heat loss term [24]. Consequently, the energy balance equation and efficiency of the solar collector are, respectively, given by [24][25][26][27][28] …”

Section: Article In Pressmentioning

confidence: 99%

“…where G is the solar irradiance, C ¼ A a =A r is the concentration ratio, ta is the effective transmittance-absorptance product, q 1 is the heat loss rate, A a and A r are, respectively, aperture and absorber areas [12,[24][25][26][27][28], and M ¼ U L T a =ðtaGCÞ which is an increasing function of U L but a decreasing function of C and is determined by the performing experiments and equipment structure. The typical values of these parameters are listed as follows [25,26]:…”

Section: Article In Pressmentioning

confidence: 99%

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Optimum performance characteristics of an irreversible solar-driven Brayton heat engine at the maximum overall efficiency

Zhang¹,

Lin²,

Chen³

2007

Renewable Energy

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Section: Article In Pressmentioning

confidence: 99%

Section: Article In Pressmentioning

confidence: 99%

Optimum performance characteristics of an irreversible solar-driven Brayton heat engine at the maximum overall efficiency

Zhang¹,

Lin²,

Chen³

2007

Renewable Energy

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“…The on-off controller is very popular both in residential and commercial buildings because of its simplicity of operation (Winn, 1982). The control logic is depicted in Fig.…”

Section: Theoretical Formulation For Analytical Solutionmentioning

confidence: 99%

“…TS-TA). However, as the actual room air temperature (T A) approaches the set-point temperature (TS), the output of the energy source decreases, there by preventing overshoot (Winn, 1982). T〓T (x′ ,t′ ,Fo,Bi1,Bi2,âLS*/(kDT), DTA/DT,)…”

Section: Theoretical Formulation For Analytical Solutionmentioning

confidence: 99%

The study of the calculation of energy consumption load for heating and cooling in building using the Laplace Transform solution

Han¹

2014

Journal of the Korean society of Fisheries Technology

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Reducing energy consumption load in residential and industrial buildings has been focused for a long time due to rapidly rising costs of heating in winter and cooling in summer time, About more than 10% of the total energy in Korea is required for residential space heating and cooling. Some works (Peavy et al., 1973, Kusuda, 1976 have been done and many methods have been developed to obtain energy consumption loads for residential buildings. This concept is first introduced using what they call room thermal response factors . Their method for procedure is that the internal showing the detail of obtaining solution process of the Laplace Transform. This study is conducted using weather data from two different locations in Korea: Seoul, Busan for both winter and summer conditions. A comparison is made for the cases of an onoff controller and a proportional controller. The weather data are processed to yield hourly average monthly values. Energy consumption load is well calculated from the solution. The result shows that there is an effect of mass on the thermal performance of heavily constructed house in mild weather conditions such as Busan. Building using proportional control experience a higher comfort level in a comparison of building using on-off control.

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“…It requires the specification of additional parameters, which could be difficult and impractical for integration in HVAC systems, and is a disadvantage of this controller. One of the important disadvantages of the optimal control strategies is the need for an appropriate model of the system for designing the controller [32][33][34][35][36][37][38][39][40][41]. Table 2 indicates advantages and disadvantages of the optimal control methods.…”

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confidence: 99%

Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

et al. 2018

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Heating, Ventilating, and Air Conditioning (HVAC) systems are the major energy-consuming devices in buildings. Nowadays, due to the high demand for HVAC system installation in buildings, designing an effective controller in order to decrease the energy consumption of the devices while meeting the thermal comfort demands in buildings are the most important goals of control designers. The purpose of this article is to investigate the different control methods for Heating, Ventilating, and Air Conditioning and Refrigeration (HVAC & R) systems. The advantages and disadvantages of each control method are discussed and finally the Fuzzy Cognitive Map (FCM) method is introduced as a new strategy for HVAC systems. The FCM method is an intelligent and advanced control technique to address the nonlinearity, Multiple-Input and Multiple-Output (MIMO), complexity and coupling effect features of the systems. The significance of this method and improvements by this method are compared with other methods.

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Controls in Solar Energy Systems

Cited by 14 publications

References 8 publications

Optimum performance characteristics of an irreversible solar-driven Brayton heat engine at the maximum overall efficiency

Optimum performance characteristics of an irreversible solar-driven Brayton heat engine at the maximum overall efficiency

The study of the calculation of energy consumption load for heating and cooling in building using the Laplace Transform solution

Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

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