A scenario-based predictive control approach to building HVAC management systems

Parisio, Alessandra; Molinari, Marco; Varagnolo, Damiano; Johansson, Karl Henrik

doi:10.1109/coase.2013.6654024

Cited by 45 publications

(49 citation statements)

References 16 publications

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“…Nolde et al in 2008 have shown how tuning the parameters of a stochastic model predictive control controller can lead to the successful medium-term scheduling of a hydro thermal storage system; the electric load and storage inflow are treated as stochastic processes [33]. Further, in the last few years, this control technique has also been studied for smart grid development in microgrid management [34], energy management systems [35,36] and for real-time market-based optimal power dispatch [37].…”

Section: Introductionmentioning

confidence: 99%

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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Energy storage is a potential alternative to conventional network reinforcement of the low voltage (LV) distribution network to ensure the grid's infrastructure remains within its operating constraints. This paper presents a study on the control of such storage devices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, where the objective is to achieve the greatest peak reduction in demand, for a given storage device specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-point controller and bench marked against a control algorithm with a perfect forecast. A specific case study, using storage on the LV network, is presented, and the results of each algorithm are compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

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Section: Introductionmentioning

confidence: 99%

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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“…As mentioned before, the online estimation problem is motivated by situations where current building occupancy levels are used for actively controlling HVAC systems, e.g., [30]. It can be derived from the previously introduced offline estimator (9) by introducing some modifications.…”

Section: Online Estimation Of the Building Occupancy Levelsmentioning

confidence: 99%

Estimation of building occupancy levels through environmental signals deconvolution

Ebadat

Bottegal

Varagnolo

et al. 2013

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

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We address the problem of estimating the occupancy levels in rooms using the information available in standard HVAC systems. Instead of employing dedicated devices, we exploit the significant statistical correlations between the occupancy levels and the CO 2 concentration, room temperature, and ventilation actuation signals in order to identify a dynamic model. The building occupancy estimation problem is formulated as a regularized deconvolution problem, where the estimated occupancy is the input that, when injected into the identified model, best explains the currently measured CO 2 levels. Since occupancy levels are piecewise constant, the zero norm of occupancy is plugged into the cost function to penalize non-piecewise constant inputs. The problem then is seen as a particular case of fused-lasso estimator by relaxing the zero norm into the 1 norm. We propose both online and offline estimators; the latter is shown to perform favorably compared to other data-based building occupancy estimators. Results on a real testbed show that the MSE of the proposed scheme, trained on a one-week-long

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“…The approach was successfully applied to many engineering applications, for instance, robot path-planning problems in [15] and the aircraft conflict detection in [17]. For the building problems, the scenario approach was investigated in [18], where samples of the external temperature, solar radiation, and room occupancy are generated by using an empirical statistic model. Approximate dynamic programming (ADP) [19] (or reinforcement learning (RL) [20] from the machine learning context) is another possibility.…”

Section: Introductionmentioning

confidence: 99%

Approximate Dynamic Programming for Building Control Problems with Occupant Interactions

Lee

Karava

et al. 2018

2018 Annual American Control Conference (ACC)

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The goal of this paper is to study potential applicability and performance of approximate dynamic programming (ADP) for building control problems. It is well known that occupants' stochastic behavior affects the thermal dynamics of building spaces. Incorporating occupant interactions in building control system designs is the main focus of this work. We apply ADP to stochastic optimal control designs for illustrative scenarios of occupant-building interactions and demonstrate its validity through a simulation study.

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A scenario-based predictive control approach to building HVAC management systems

Cited by 45 publications

References 16 publications

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

Estimation of building occupancy levels through environmental signals deconvolution

Approximate Dynamic Programming for Building Control Problems with Occupant Interactions

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