Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study

Parisio, Alessandra; Varagnolo, Damiano; Molinari, Marco; Pattarello, Giorgio; Fabietti, Luca; Johansson, Karl Henrik

doi:10.3182/20140824-6-za-1003.02629

Cited by 44 publications

(38 citation statements)

References 20 publications

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“…MPC algorithms have increasingly become attractive options in building control [5,6] due to their ability to utilize real-time weather and occupant information to minimize energy consumption. In the proposed solution, the RTU coordination problem over a given time horizon is formulated as a discrete-time switched affine quadratic regulation (SAQR) problem with mode-dependent switching costs and solved by the dynamic programming method with complexity reduction techniques.…”

Section: Introductionmentioning

confidence: 99%

A switched dynamic programming approach towards optimal control of multiple rooftop units

Putta

Kim

Cai

et al. 2015

2015 American Control Conference (ACC)

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Buildings served by multiple rooftop units (RTUs) are widely prevalent in retail and food industries. Till date, most of such buildings utilize plain feedback or heuristic strategies to determine the RTU operations. We present a switched dynamic programming based approach for scheduling rooftop unit operations in an optimal manner. Using a mid-size restaurant served by multiple RTUs as an example, a switched affine dynamic system model is first presented. The RTU coordination control problem is then formulated as a switched optimal control problem with nontrivial switching cost. A dynamical programming based method is proposed for the solution of the switched optimal control problem, which has reasonable computational complexity and can be implemented as a model predictive control algorithm. The proposed method is evaluated through simulations, and its strengths and limitations are discussed.

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Section: Introductionmentioning

confidence: 99%

A switched dynamic programming approach towards optimal control of multiple rooftop units

Putta

Kim

Cai

et al. 2015

2015 American Control Conference (ACC)

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“…A case study in which artificial neural network (ANN) models of a residential house located in Ontario, Canada are developed and calibrated with the data measured from the location is addressed in [19]. In [35], Parisio et al present an experimental case study of a scenario-based MPC for HVAC systems. In [36] an experimental implementation of whole building MPC with zone based thermal comfort adjustments is presented.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

et al. 2018

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Abstract:The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.

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“…These two issues are addressed in [15], [16], where authors consider scenario-based approximations of a chance constrained MPC problem that accounts for uncertainty by extracting the scenarios from general probability distributions, thus not restricted to be Gaussian [17].…”

Section: Introductionmentioning

confidence: 99%

Control of HVAC systems via scenario-based explicit MPC

Parisio

Fabietti

Molinari

et al. 2014

53rd IEEE Conference on Decision and Control

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Abstract-Improving energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems is a primary objective for the society. Model Predictive Control (MPC) techniques for HVAC systems have recently received particular attention, since they can naturally account for several factors, such as weather and occupancy forecasts, comfort ranges and actuation constraints. Developing effective MPC based control strategies for HVAC systems is nontrivial, since buildings dynamics are nonlinear and affected by various uncertainties. Further, the complexity of the MPC problem and the burden of on-line computations can lead to difficulties in integrating this scheme into a building management system.We propose to address this computational issue by designing a scenario-based explicit MPC strategy, i.e., a controller that is simultaneously based on explicit representations of the MPC feedback law and accounts for uncertainties in the occupancy patterns and weather conditions by using the scenarios paradigm. The main advantages of this approach are the absence of a-priori assumptions on the distributions of the uncertain variables, the applicability to any type of building, and the limited on-line computational burden, enabling practical implementations on low-cost hardware platforms.We illustrate the practical implementation of the proposed explicit MPC controller on a room of a university building, showing its effectiveness and computational tractability. I. INTRODUCTIONHeating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable indoor comfort levels; reports indicate that HVAC systems in developed countries contribute for approximately one fifth of the total national energy usages [1]. Current practice shows its limits, with potential energy savings achievable by using systematic building management being estimated from 5% to 30% of the total consumptions [2], [3]. An effective controller for HVAC systems should incorporate time-dependent energy costs, bounds on the control actions, comfort requirements, as well as account for system uncertainties, e.g., weather conditions and occupancy. A natural scheme that achieves the systematic integration of all the aforementioned elements is the Model Predictive Control (MPC) [4].Simulations in [5], [6], as well as the experimental results on real buildings reported in [7], [8], [9], show that MPC schemes can yield better comfort levels and energy use performance than current practices.

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Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study

Cited by 44 publications

References 20 publications

A switched dynamic programming approach towards optimal control of multiple rooftop units

A switched dynamic programming approach towards optimal control of multiple rooftop units

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

Control of HVAC systems via scenario-based explicit MPC

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