Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part II - Potential of predictive strategy

Viot, Hugo; Sempey, Alain; Mora, Laurent; Batsale, Jean‐Christophe; Malvestio, J.

doi:10.1016/j.enbuild.2018.04.062

Cited by 29 publications

(10 citation statements)

References 13 publications

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“…The model's parameters (R 1 , C 1 ), (R 2 , C 2 ), and (R 3 , C 3 ) respectively designate the thermal resistance and capacity of the first wall, the indoor air, and the second wall. The model can be expressed as a linear stochastic differential equation written into a matrix form for state-space representation by applying Kirchoff's balance laws to the circuit [75]. In addition, it includes a state equation and an output equation:…”

Section: Gray Box Model (Gbm)mentioning

confidence: 99%

Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

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Section: Gray Box Model (Gbm)mentioning

confidence: 99%

Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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“…Several research streams recently dealt with the efficient operation of equipment and energy resources in smart buildings, by investigating different fields. Examples are the optimization of HVAC systems based on Model Predictive Control (MPC) algorithms or Neural Networks (NNs) [14][15][16][17], or the application of flexible control strategies, Demand Side Management (DSM) actions, and Demand Response (DR) programs [2,[18][19][20]. The application of such control strategies is implemented in single family houses through the so-called Home Energy Management Systems (HEMSs), such as well as in large buildings, by means of Building Energy Management Systems (BEMSs).…”

Section: Distributed Management Of Energy Resourcesmentioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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The application of the most recent advances of the Internet-of-Things (IoT) technology to the automation of buildings is emerging as a promising solution to achieve greater efficiencies in energy consumption, and to allow the realization of sustainable models. The application of IoT has been demonstrated as effective in many fields, such as confirmed, for instance, by the Industry 4.0 concepts, which are revolutionizing modern production chains. By following this approach, the use of distributed control architectures and of IoT technologies (both wired and wireless) would result in effective solutions for the management of smart environments composed of groups of buildings, such as campuses. In this case, heterogeneous IoT solutions are typically adopted to satisfy the requirements of the very diverse possible scenarios (e.g., indoor versus outdoor coverage, mobile versus fixed nodes, just to mention a few), making their large-scale integration cumbersome. To cope with this issue, this paper presents an IoT architecture able to transparently manage different communication protocols in smart environments, and investigates its possible application for the monitoring and control of distributed energy resources in a smart campus. In particular, a use–case focused on the integration of the Long Range Wide Area Network (LoRaWAN) technology is considered to cope with heterogeneous indoor and outdoor communication scenarios. The feasibility analysis of the proposed solution is carried out by computing the scalability limits of the approach, based on the proposed smart campus data model. The results of the study showed that the proposed solution would be able to manage more than 10,000 nodes. An experimental validation of the LoRaWAN technology confirms its suitability in terms of coverage and latency, with a minimum LoRaWAN cell coverage range of 250 m, and a communication latency of about 400 ms. Finally, the advantages of the proposed solution in the supervision and management of a PV system are highlighted in a real-world scenario.

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“…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. A similar approach was taken by Viot et al [41], using an MPC in an office building with a cost function that incorporated comfort (an optimal temperature within a comfort interval) and energy consumption costs. Sturzenegger et al [42] implemented an office building HVAC system MPC which optimised the costs of operational energy usage and penalized comfort as constraint.…”

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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Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part II - Potential of predictive strategy

Cited by 29 publications

References 13 publications

Use of Machine Learning Methods for Indoor Temperature Forecasting

Use of Machine Learning Methods for Indoor Temperature Forecasting

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

Economic model predictive control for demand flexibility of a residential building

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