A model-assisted adaptive controller fine-tuning methodology for efficient energy use in buildings

Giannakis, Georgios B.; Kontes, Georgios; Kosmatopoulos, Elias B.; Rovas, Dimitrios

doi:10.1109/med.2011.5983175

Cited by 9 publications

(12 citation statements)

References 28 publications

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“…Further examples for systems proposed to perform an energy-use related prediction procedure can be reviewed in [120][121][122]. Usually, these reviewed EMS-in-Bs aim to minimize energy-use [120], produce an efficient energy-use in a building [121], and/ or design an efficient energy management method [99].…”

Section: ) Prediction-oriented Ems-in-bsmentioning

confidence: 99%

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Energy Management Systems and Strategies in Buildings Sector: A Scoping Review

et al. 2021

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Energy management systems in buildings (EMSs-in-Bs) play key roles in energy saving and management to which an efficient energy management system in buildings (EMS-in-Bs) design contributes. Different scope-based designs of EMS-in-Bs are reviewed. The objective is to highlight different scope-based designs of EMS-in-Bs in which scopes of reviewed papers aim to implement a function of, for example, "monitor energy performance", "estimate energy-use", or "control energy-use". This paper aims to constitute a comprehensive conception of how efficient such an EMS-in-Bs to perform more than one scope (i.e., function). Meaning, is the proposed EMS-in-Bs able to perform several sequential functions? This paper's contribution is to give a function-focused EMS's review utilizing the scope of reviewed papers. That is, reviewed papers are classified based on the scope/function the selected EMS-in-Bs is designed for. This could help select an EMS-in-Bs to perform certain scope/function(s). Another contribution is that, numerous EMSsin-Bs are reviewed in a classified way so that the most adequate EMS-in-Bs for a certain scenario considering the performed scopes/functions e.g., "monitor" are highlighted. Findings showed that "control-optimize"functioned EMS-in-Bs achieved highest energy-saving rates ~30% compared to "estimate-predict" with 10%. Findings, insights given by reviewed studies, current problems faced, future directions, and remarks are drawn in conclusion. Analysis done on reviewed papers has found that the highest and lowest averagedenergy saving rates were obtained with papers whose their scopes are implementing "control"-with-"optimize" and "estimate"-with-"predict", respectively. Energy saving rates for these two classes of scopes have been equal to 22.57% and 10%, respectively. We recommend that there is a need to enhance the estimation-and prediction-related EMS-in-Bs to achieve a higher energy saving rate.

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Section: ) Prediction-oriented Ems-in-bsmentioning

confidence: 99%

“…It is useful to predict energy demand for three types of buildings: heating-, cooling-dominated, and thermally balanced. The authors in [121] have proposed a model using controller parameters that affect the behavior of measures. It has used a logic controller method in which its parameters are adaptively adjusted.…”

Section: ) Prediction-oriented Ems-in-bsmentioning

confidence: 99%

Energy Management Systems and Strategies in Buildings Sector: A Scoping Review

et al. 2021

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“…The Cognitive Adaptive Optimization (CAO) algorithm is used to efficiently explore the large decision space and identify "good" controllers to be applied to the real building, with the goal of maximization of the "Net Expected Benefit" (NEB) [11]. For simple buildings, the potential of adapting the CAO algorithm for building control has been investigated in [8].…”

Section: The Pebble Projectmentioning

confidence: 99%

“…a set of proper gains θ) is designed. The CAO [8] algorithm is a local search algorithm, used to improve an initial controller provided by an expert or by an Approximate Optimal Control design process (AOC) such as in [10]. Table 3.…”

Section: Cognitive Adaptive Optimizationmentioning

confidence: 99%

Simulation-assisted building energy performance improvement using sensible control decisions

Pichler

Dröscher

Schranzhofer

et al. 2011

Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings

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The building sector contributes significantly to global energy consumption and emission of greenhouse gases. Thermal insulation along with installation of energy-efficient building systems can reduce energy needs while preserving or improving occupant comfort levels. Still sensible control decisions, to harmoniously and effectively operate all building thermal systems, can be used to further improve building energy performance and/or thermal comfort. In this article, a simulation-assisted methodology is presented to automatically generate such decisions. There are two ingredients to our approach: a thermal simulation model-a surrogate of the real building-used to evaluate the effects of potential decisions; and, a cognitive adaptive optimization algorithm used to intelligently search for the "best" control decision. A user-defined cost function is used to compare various decision strategies. Corroborating simulation results are presented to quantify the expected benefits of the proposed approach.

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“…Integrating all of these single components require a tedious manual effort by HVAC system installers to tune all these set points: apart from the enormous tuning effort [12], it is difficult to explicitly account for changing conditions, e.g., individual comfort of occupants or their occupancy patterns. Very often, thermal discomfort often leads to constant correction of temperature set-points by the users, causing increased energy consumption [13,14]. Thus, it is necessary to develop a model-based approach with the ability to integrate the human thermal comfort along with various HVAC components.…”

Section: Introductionmentioning

confidence: 99%

A University Building Test Case for Occupancy-Based Building Automation

et al. 2018

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Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level set-point control and low-level Proportional-Integral-Derivative (PID) controls, there is a need for overcoming current control fragmentation without disrupting the standard hierarchy. In this work, we propose a model-based approach to achieve these goals. In particular: the set-point control is based on a predictive HVAC thermal model, and aims at optimizing thermal comfort with reduced energy consumption; the standard low-level PID controllers are auto-tuned based on simulations of the HVAC thermal model, and aims at good tracking of the set points. One benefit of such control structure is that the PID dynamics are included in the predictive optimization: in this way, we are able to account for tracking transients, which are particularly useful if the HVAC is switched on and off depending on occupancy patterns. Experimental and simulation validation via a three-room test case at the Delft University of Technology shows the potential for a high degree of comfort while also reducing energy consumption.

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A model-assisted adaptive controller fine-tuning methodology for efficient energy use in buildings

Cited by 9 publications

References 28 publications

Energy Management Systems and Strategies in Buildings Sector: A Scoping Review

Energy Management Systems and Strategies in Buildings Sector: A Scoping Review

Simulation-assisted building energy performance improvement using sensible control decisions

A University Building Test Case for Occupancy-Based Building Automation

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