Predictive control techniques for energy and indoor environmental quality management in buildings

Κολοκότσα, Δ.; Pouliezos, A.; Stavrakakis, G.; Lazos, C.

doi:10.1016/j.buildenv.2008.12.007

Cited by 165 publications

(96 citation statements)

References 14 publications

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“…For the sake of comparison, we consider a standard approach that uses set-points with set-back conditions to save energy and uses controllers to track these set-points as tightly as possible [18,19,7,6]. This leads to the following formulation:…”

Section: Energy Minimizationmentioning

confidence: 99%

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Real-Time Optimization Strategies for Building Systems

Zavala

2012

Ind. Eng. Chem. Res.

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We analyze different optimization strategies for real-time energy management in building systems. We have found that exploiting building-wide multivariable interactions between CO 2 , humidity, pressure, occupancy, and temperature leads to significant reductions of energy intensity. Our analysis indicates that it is possible to obtain energy savings of more than 50% compared with traditional control strategies. The exploitation of the thermal comfort zone has been found to be the most relevant factor driving energy savings. We discuss strategies to handle multiple competing objectives as well as economic functions and we analyze financial incentives provided by real-time prices and existing market designs. Numerical experiments are provided to support the claims.

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Section: Energy Minimizationmentioning

confidence: 99%

“…While diverse studies have been reported [16,11,12,21,4,29,18,26], traditional control objectives (e.g., set-point tracking) are often used. In addition, previous studies have focused on independent components of the building such as air-handling units, chillers, ventilation rates, air quality, and temperature.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimization Strategies for Building Systems

Zavala

2012

Ind. Eng. Chem. Res.

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“…Description of Input/Output and exogenous variables the model are omitted since they result from classical arguments that can be found in existing references (Virk and Cheung [1995], Mustafaraja et al [2010], Gwerder and Tödtli [2009], Kolokotsa et al [2009]). …”

Section: Dynamic Model Of a Zonementioning

confidence: 99%

“…This is witnessed by the many recent works adopting such predictive solutions to address various related problems such as the control of micro-cogeneration system in domestic applications (Collazos et al [2009]); temperature control (Gwerder and Tödtli [2009]), combined control of temperature and ventilation (Yuan and Perez [2006]). In Kolokotsa et al [2009], an MPC controller has been designed that involves a BEMS system to regulate indoor temperature, dioxide carbon concentration and internal humidity ratio.…”

Section: Introductionmentioning

confidence: 99%

Unified NMPC for Multi-Variable Control in Smart Buildings

Lamoudi

Alamir

Béguery

2011

IFAC Proceedings Volumes

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In this paper, the problem of minimizing energy consumption of a building zone under pre-assigned multi-variable comfort conditions and changing energy rate is addressed. The solution involves the use of a parameterized multi-variable Nonlinear Model Predictive Control (NMPC) that manages the actuation of heating/cooling, ventilation, lighting and blinds devices. Simulations of the resulting closed-loop in winter and summer seasons under varying rate profile are proposed to assess its efficiency. Moreover, a sensitivity analysis is conducted to show how the comfort level assignment impacts the level of energy consumption.

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“…Differently from other simulation-based control strategy for energy-efficient control of microgrids [9], [10], [11], the proposed one aims at solving iteratively the optimal control problem defined by the Hamilton-Jacobi-Bellman equation. In contrast with Model Predictive Control (MPC) strategies [12], [13], the resulting solution is a closed-loop solution, which is shown, via extensive simulations, to be robust to different weather conditions. Comparisons with alternative strategies reveal that the proposed supervisory strategy efficiently sensibly improve independence of the microgrid with respect to the main grid, and guarantees at the same time thermal comfort of the occupants.…”

Section: Introductionmentioning

confidence: 99%

A supervisory approach to microgrid demand response and climate control

Korkas

Baldi

Michailidis

et al. 2016

2016 24th Mediterranean Conference on Control and Automation (MED)

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Abstract-Microgrids equipped with small-scale renewableenergy generation systems and energy storage units offer challenging opportunity from a control point of view. In fact, in order to improve resilience and enable islanded mode, microgrid energy management systems must dynamically manage controllable loads by considering not only matching energy generation and consumption, but also thermal comfort of the occupants. Thermal comfort, which is often neglected or oversimplified, plays a major role in dynamic demand response, especially in front of intermittent behavior of the renewable energy sources. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in a microgrid composed of a block of buildings, a photovoltaic array, a wind turbine, and an energy storage unit. In order to address the large-scale nature of the problem, the proposed control strategy adopt a two-level supervisory strategy: at the lower level, each building employs a local controller that processes only local measurements; at the upper level, a centralized unit supervises and updates the three controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. Comparisons with alternative strategies reveal that the proposed supervisory strategy efficiently manages the demand response so as to sensibly improve independence of the microgrid with respect to the main grid, and guarantees at the same time thermal comfort of the occupants.

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Predictive control techniques for energy and indoor environmental quality management in buildings

Cited by 165 publications

References 14 publications

Real-Time Optimization Strategies for Building Systems

Real-Time Optimization Strategies for Building Systems

Unified NMPC for Multi-Variable Control in Smart Buildings

A supervisory approach to microgrid demand response and climate control

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