Distributed MPC for Thermal Comfort in Buildings with Dynamically Coupled Zones and Limited Energy Resources

Barata, Filipe; Neves-Silva, Rui

doi:10.1007/978-3-642-54734-8_34

Cited by 3 publications

(4 citation statements)

References 12 publications

(16 reference statements)

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“…At the same time, it is important to study further the robust, stochastic or scenario-based formulations, which allow the uncertainty related to the forecast of disturbances in the optimization problem to be considered. Important steps in this direction were already made in [50,51,74,92,108,145]. Besides, benchmarking strategies for experimental applications should be defined.…”

Section: Critical Discussion and Conclusionmentioning

confidence: 99%

“…The mutual interactions among factors are regarded as unknown external perturbations to the model using this approach; the stronger the correlations of performance between these components, the more the reliability of the controller can be affected. The distributed MPC approach of Figure 13c has been considered an attractive solution for large-scale dynamically coupled building systems by many researchers [92,[103][104][105][106][107][108][109], because it incorporates positive features of both centralized and decentralized configurations, merged in a single controller. This cooperative behavior of every individual controller improves the global control performance when compared to the decentralized structure.…”

Section: Bas and Mpc Control Architecturementioning

confidence: 99%

“…This configuration is the less frequent for building and its HVAC system control purposes. It is mainly used when the control goals focus on the peak values [108,[139][140][141][142][143][144][145], such as reducing or shifting the power peak load or minimizing the maximum daily PPD value [75].…”

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confidence: 99%

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Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

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Section: Critical Discussion and Conclusionmentioning

confidence: 99%

Section: Bas and Mpc Control Architecturementioning

confidence: 99%

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Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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“…The electro thermal-electrical modular approach used to develop the house models is described in detail in [19]. So, in this work are only showed the main equations.…”

Section: Dynamical Modelsmentioning

confidence: 99%

Demand Side Management Energy Management System for Distributed Networks

Barata¹,

Igreja²,

Neves-Silva³

2016

Technological Innovation for Cyber-Physical Systems

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This paper is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in household or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach that includes an auction and a shifting load strategy. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. The developed solution is explained and applied to different scenarios wherein the results illustrate the benefits of the proposed approach.

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