Predictive control methods to improve energy efficiency and reduce demand in buildings

Salsbury, Timothy I.; Mhaskar, Prashant; Qin, S. Joe

doi:10.1016/j.compchemeng.2012.08.003

Cited by 72 publications

(45 citation statements)

References 38 publications

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“…Each method gives an estimate of a building-specific setpoint trajectory that gives a "flat" cooling load profile during a specified demand-limiting period [7]. Another possible technique is the "5-period division" scheduling [8]. Using this technique, the day is divided into five periods, according to occupancy, with specific operation in each period.…”

Section: Introductionmentioning

confidence: 99%

Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings

Garnier

Eynard

Caussanel

et al. 2015

Applied Soft Computing

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International audienceIn France, buildings account for a large part of the energy consumption and carbon emissions. Both are mainly due to Heating, Ventilation and AirConditioning (HVAC) systems. Because older, oversized or poorly maintained systems may be using more energy and costing more to operate than necessary, new management approaches are needed. In addition, energy efficiency can be improved in central heating and cooling systems by introducing zoned operation. So, the present work deals with the predictive control of multizone HVAC systems in non-residential buildings. First, a real non-residential building located in Perpignan (south of France) has been modelled using the EnergyPlus software. We used the Predicted Mean Vote (PMV) index as a thermal comfort indicator and developed low-order ANN-based models to be used as controller's internal models. A genetic algorithm allowed the optimization problem to be solved. Using the proposed strategy, the operation of all the HVAC subsystems is optimized by computing the right time to turn them on and off, in both heating and cooling modes. Energy consumption is minimized and thermal comfort requirements are met. In order to appraise the proposed management strategy, it has been compared to basic scheduling techniques. The simulation results highlight the pertinence of a predicitive approach for multizone HVAC systems management

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Section: Introductionmentioning

confidence: 99%

Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings

Garnier

Eynard

Caussanel

et al. 2015

Applied Soft Computing

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“…Several studies show that predictive controllers may significantly decrease energy consumptions when endowed with real-time measurements, weather conditions, and occupancy forecasts [7,16,24,10,9]. This is confirmed by experimental results on real buildings, where MPCs yield better energy use and comfort levels performance than current practices [26,12].…”

Section: Introductionmentioning

confidence: 64%

Randomized Model Predictive Control for HVAC Systems

Parisio

Varagnolo

Risberg

et al. 2013

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

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Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and Indoor Air Quality (IAQ) levels, essentials for occupants well-being. Since performing this task implies high energy requirements, there is a need for improving the energetic efficiency of existing buildings. A possible solution is to develop effective control strategies for HVAC systems, but this is complicated by the inherent uncertainty of the to-be-controlled system. To cope with this problem, we design a stochastic Model Predictive Control (MPC) strategy that dynamically learns the statistics of the building occupancy and weather conditions and uses them to build probabilistic constraints on the indoor temperature and CO 2 concentration levels. More specifically, we propose a randomization technique that finds suboptimal solutions to the generally non-convex stochastic MPC problem. The main advantage of this method is the absence of apriori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the proposed approach by means of numerical simulations and real * tests on a student laboratory, and show its practical effectiveness and computational tractability.

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“…These studies show that MPC can significantly decrease energy consumption when exploiting the information on weather conditions and occupancy forecasts. Some of the approaches rely on deterministic forecasts and neglect uncertainty, [8], [9], [11], [19]. More recently, alternative approaches have been proposed, which explicitly account for the uncertainty affecting the system and head for chance constrained solutions where e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimal building climate control: a solution based on nested dynamic programming and randomized optimization

Deori

Giulioni

Prandini

2014

53rd IEEE Conference on Decision and Control

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We consider the problem of minimizing the electric energy consumption in the climate control of a building. More specifically, we suppose that the reference temperature in the building can be modulated to some extent during the day, so as to reduce the power request while limiting the introduced level of discomfort. To account for the uncertain building occupancy, we formulate the optimal energy management problem as a chance constrained optimization problem where one seeks for a state feedback policy that is robust over all occupancy realizations except for a set of predefined (small) probability. By exploiting the different time scales of the control and disturbance inputs, an algorithmic solution based on nested dynamic programming and randomized optimization is put forward and its parallel GPU implementation is discussed. A numerical example shows the efficacy of the approach

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Predictive control methods to improve energy efficiency and reduce demand in buildings

Cited by 72 publications

References 38 publications

Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings

Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings

Randomized Model Predictive Control for HVAC Systems

Optimal building climate control: a solution based on nested dynamic programming and randomized optimization

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