Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort

Mathews, E.H.; Arndt, Deon C.; Piani, C.; Heerden, Eugene Van

doi:10.1016/s0306-2619(99)00035-5

Cited by 67 publications

(35 citation statements)

References 3 publications

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“…A standard PID controller is a control mechanism [26], widely used in buildings engineering for heating control [5][6][7][8][9]. That is why it was considered as the reference controller and was the basic component of the proposed advanced control schemes (PID-MPC and PID-FLC schemes).…”

Section: Pid Control Schemementioning

confidence: 99%

“…Mathews et al (2008) [6], Levermore et al (1992) [7], Bernard et al (1982) [8], Kolokotsa et al (2005) [9], Ben-Nakhi et al (2001) [10], Kalogirou et al (2000) [11] and Gonzalez et al (2005) [12] presented works related to energy management, cost strategies and energy consumption forecast, while Chen et al (2006) [13], Calvino et al (2004) [14], Kummert et al (2001) [15], Morel et al (2000) [16], Nygard (1990) [17], Lute et al (2000) [18], Liang et al (2005) [19] and Argiriou et al (2001) [20] focus on fuzzy, neural network, optimal or predictive control of thermal conditions in buildings. However, energy management in buildings is not really correlated with energy savings and clear strategies to optimize the use of several (fossil and renewable) energy resources are not defined.…”

Section: Introductionmentioning

confidence: 99%

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Heating control schemes for energy management in buildings

Paris

Eynard

Grieu

et al. 2010

Energy and Buildings

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because of both the global energy crisis and the necessary improvement of energy efficiency in buildings, one of the largest sectors of energy consumption and greenhouse gases emissions, a strategy allowing managing energy resources is proposed. Its aim is reducing energy consumption and promoting the use of renewable energy, while ensuring thermal comfort, when heating "multi-energy" buildings, thanks to indoor temperature control schemes. Three schemes (based on a commonly-used PID controller and on the combination of PID and model predictive or fuzzy controllers) were tested in simulation, using dynamic models describing the thermal behavior of a building, and fully met the management strategy's requirements, especially reducing the consumption of fossil energy. Three criteria describing the way energy is used and controlled in real-time were defined with the aim of evaluating the control schemes performance and adapting the strategy to the specific use of a building. The PID-MPC provided the best results while the PID-FLC proved to be a very good compromise, thanks to both the flexibility and the adaptability offered by fuzzy logic, between the easy-todevelop but not-very-efficient PID and the efficient but hard-to-develop PID-MPC.

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Section: Pid Control Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heating control schemes for energy management in buildings

Paris

Eynard

Grieu

et al. 2010

Energy and Buildings

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“…Components are often modelled separately, as illustrated by this overview on recent implementations of modelling and control strategies for HVAC systems: a chiller alone [11], a radiator alone [12], an AHU alone [13,14,15], a heat pump alone [16], AHUs with multiple zones [17,18,19], floor heating with multiple zones [20]. Some efforts toward HVAC integration can be recognized in works such as [21] (chiller and AHU), [22,23] (AHU with chiller, fans and multiple zones), [24] (boiler, heat pump and chiller), even if many other HVAC components are still not integrated. This overview shows that HVAC system-wide control integration is far from being reached.…”

Section: Introductionmentioning

confidence: 99%

An integrated control-oriented modelling for HVAC performance benchmarking

Satyavada

Baldi

2016

Journal of Building Engineering

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Energy efficiency in building heating, ventilating and air conditioning (HVAC) equipment requires the development of accurate models for testing HVAC control strategies and corresponding energy consumption. In order to make the HVAC control synthesis computationally affordable, such controloriented models are typically a simplified version of more elaborate building simulation environments (e.g. EnergyPlus, Modelica, TRNSYS). Despite their simplicity, control-oriented models must effectively catch all the interactions between HVAC equipment, in order achieve system integration and avoid fragmentation in HVAC modelling and control synthesis, which is one of the main causes of suboptimal performance in the building sector. In this work we propose an integrated control-oriented modelling for HVAC performance benchmarking. The following HVAC equipment and their interaction are modelled: condensing boiler, radiator, air handling unit, heat pump, chiller, fan, pump, pipe, duct, and multiple thermal zones. The proposed modelling approach is modular so that the user can add and remove as many components as desired, depending on the building to be modelled. Appropriate procedures for synthesis of control strategies and benchmarking are proposed. Extensive simulations demonstrate the effectiveness of the 1 © 2016 Manuscript version made available under CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ Link to formal publication Journal of Building Engineering (Elsevier): http://doi.org/10. 1016/j.jobe.2016.04.005 proposed methodology, which can lead to improvements in occupants' thermal comfort while at the same time consistently attaining energy savings.

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“…Standard control schemes, such as "on/off" and PID, are widely used in building engineering [9,10,11]. As an example, On/off controllers are used for indoor temperature regulation but, in this case, energy consumption is high because of both significant fluctuations and frequent setpoint overshoots.…”

Section: Introductionmentioning

confidence: 99%

Hybrid PID-fuzzy control scheme for managing energy resources in buildings

Paris

Eynard

Grieu

et al. 2011

Applied Soft Computing

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both indoor temperature regulation and energy resources management in buildings require the design and the implementation of efficient and readily adaptable control schemes. One can use standard schemes, such as "on/off" and PID, or "advanced" schemes, such as MPC (Model Predictive Control). Another approach would be considering artificial intelligence tools. In this sense, fuzzy logic allows controlling temperature and managing energy sources, taking advantage of the flexibility offered by linguistic reasoning. With this kind of approaches, both the specific use of a building and the specificities of a proposed energy management strategy can be easily taken into account when designing or adjusting the control scheme, without having to model the process to be controlled. PID controllers being commonly used in buildings engineering, the proposed control scheme is built on the basis of a PID controller. This allows implementing the scheme even if a control system based on such a controller is already in use. So, a hybrid PID-fuzzy scheme is proposed for managing energy resources in buildings, as the combination of two usual control structures based on PID and fuzzy controllers: the "parallel" structure (according to the current dynamical state of the considered process, either the PID or the fuzzy controller is selected) and the "fuzzy supervision" of a PID controller. To test the scheme in simulation, a building mock-up has been built, instrumented and modeled. Finally, criteria describing the way energy is used and controlled in real-time have been defined with the aim of evaluating both the proposed strategy and the control scheme performance.

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Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort

Cited by 67 publications

References 3 publications

Heating control schemes for energy management in buildings

Heating control schemes for energy management in buildings

An integrated control-oriented modelling for HVAC performance benchmarking

Hybrid PID-fuzzy control scheme for managing energy resources in buildings

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