Energy efficient model predictive building temperature control

Wallace, Matt; McBride, R. B.; Aumi, Siam; Mhaskar, Prashant; House, John M.; Salsbury, Timothy I.

doi:10.1016/j.ces.2011.07.023

Cited by 66 publications

(43 citation statements)

References 20 publications

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“…This tendency is supported not only by simulations (Treado and Chen, 2013;Wallace et al, 2012;Fadzli Haniff et al, 2013), but also by some experimental results on real buildings Široký et al, 2011;Parisio et al, 2013b). Model Predictive Controls (MPCs) may truly yield better comfort levels and energy use performance than current practices do.…”

Section: Literature Reviewmentioning

confidence: 58%

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

Parisio

Varagnolo

Molinari

et al. 2014

IFAC Proceedings Volumes

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Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.

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Section: Literature Reviewmentioning

confidence: 58%

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

Parisio

Varagnolo

Molinari

et al. 2014

IFAC Proceedings Volumes

View full text Add to dashboard Cite

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“…These classical methods have been investigated extensively in HVAC lower-loop controls [4][5][6], such as room temperature control [7][8][9] and supply air temperature control [10][11][12]. Despite their popularity, these classical methods do not perform optimally.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems

2017

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Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using SketchUp. Using OpenStudio, the HVAC system is installed in the office. The control schemes (ideal thermal comfort, a traditional control and the RL control) are implemented in MATLAB. Using the Building Control Virtual Test Bed (BCVTB), the control of the thermostat schedule during each sample time is implemented for the office in EnergyPlus alongside local weather data. Results from training and validation indicate that the RL controller improves thermal comfort by an average of 15% and energy efficiency by an average of 2.5% as compared to other strategies mentioned.

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“…As a rule, to solve these problems, engineers apply computer simulation models [15,16]. The use of computer simulation provides a small labor cost and reduces time of experimentation.…”

Section: Simulation Objectmentioning

confidence: 99%

Research of Energy Saving Mode of Building Distributed Control System Under Influence of Many Disturbing Factors

Morozov

Ushakov

2016

MATEC Web Conf.

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Abstract.A model of the building heating system, consisting of energy source, a distributed automatic control system, elements of individual heating unit and heating system is designed. Application Simulink of mathematical package Matlab is selected as a platform for the model. There are the specialized application Simscape libraries in aggregate with a wide range of Matlab mathematical tools allow to apply the "acausal" modeling concept. Implementation the "physical" representation of the object model gave improving the accuracy of the models. Advanced weather block allowed to investigate the efficiency of the control system with dynamically changing the numerous factors affecting the building energy consumption for maintaining optimal climate parameters. The conducted research of heating system and automatic control system allowed estimate efficiency of control system regulating process. The results of research of thermal condition premises, situated in the different sides of the building facade are given. The conclusions about the possibility of increasing the operational efficiency of heating system in the implementation of distributed control systems are presented.

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Energy efficient model predictive building temperature control

Cited by 66 publications

References 20 publications

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

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

A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems

Research of Energy Saving Mode of Building Distributed Control System Under Influence of Many Disturbing Factors

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