Implementation of cooperative Fuzzy model predictive control for an energy-efficient office building

Killian, Michaela; Kozek, Martin

doi:10.1016/j.enbuild.2017.11.021

Cited by 37 publications

(19 citation statements)

References 32 publications

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“…Xia et al developed new sufficient stability conditions for the fuzzy MPC through the technique of slack matrices [21]. Afterwards, T-S fuzzy model-based predictive control has been successfully applied in many processes, such as energy-efficient office building [22] and power generation [23]. Although the nonlinear behavior or parameter variation of the system have been considered in the aforementioned methods, the appearance of disturbances, which is ubiquitous in the industry process, can cause severe degradation of the control performance [24].…”

Section: A Literature Reviewmentioning

confidence: 99%

Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

Zhu

Nguang

2020

IEEE Access

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Section: A Literature Reviewmentioning

confidence: 99%

Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

Zhu

Nguang

2020

IEEE Access

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“…There have been few successful demonstrations of MPC implementations in buildings, because the financial benefits of MPC implementation are still smaller than the total costs [39,40]. The main challenges in designing a robust and resilient MPC which can be adapted to different buildings are the consideration of the stochastic nature of disturbances such as weather and occupancy, the effort and cost of modelling methods, and the conflicting nature of control objectives [39,40]. As an example for conflicting control objectives, in an experimental study from Killian et al [39], optimal control was applied to an office building to minimize the primary energy consumption for heating and cooling while maximizing users' thermal comfort.…”

Section: 2mentioning

confidence: 99%

Economic model predictive control for demand flexibility of a residential building

Finck

Zeiler

2019

Energy

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Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy.In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from -0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

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“…A 26% reduction in heating demand in residential buildings, by using the adaptive predictive control of thermo-active building systems, was presented in (Schmelas et al 2017), whereas in (Pang et al 2018) a 16% reduction in the cooling demand in case of an applied MPC to a radiant slab cooling system was reported. In commercial buildings, the implementation of MPC in building services has resulted in yearly energy savings of between 31% and 36% (Killian and Kozek 2018). Somewhat smaller savings are presented in (Bamdad et al 2018), where the yearly energy saving was up to 26%.…”

Section: Introductionmentioning

confidence: 99%

Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings

et al. 2019

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In this paper, the possibilities of developing machine learning based data-driven models for the short-term prediction of indoor temperature within prediction horizons ranging from 1 hour up to 12 hours are systematically investigated. The study was based on a TRNSYS emulation of a residential building heated by a heat pump, combined with measured weather data for a typical winter season in Ljubljana, Slovenia. Autoregressive models with exogenous inputs (ARX), neural network models (NN), and extreme learning machine models (ELM) are considered. The results confirm the finding that nonlinear models, particularly the NN model trained by regularization, consistently outperform linear models in both fitting and generalization performance, so they are the recommended choice as predictive models. The availability of future weather data considerably improved the predictive performance of all the tested models. Besides data about the future outdoor temperature, also data about future expected solar radiation significantly improve predictions of temperature in buildings. The linear models required embedding dimensions of 24 hours for accurate predictions, whereas the nonlinear models were not very sensitive to the use of past data. Nonlinear models required about three months of training data to reach good predictive performance, whereas the linear models converged to accurate predictions within six weeks. The RMSE prediction errors, averaged over all the data sets and all the prediction horizons, are within the range between 0.155°C for the linear ARX model (in the case of no future available weather data), and 0.065°C for the neural network model (in the case of available future weather data).

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Implementation of cooperative Fuzzy model predictive control for an energy-efficient office building

Cited by 37 publications

References 32 publications

Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

Economic model predictive control for demand flexibility of a residential building

Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings

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