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Data-driven approach for robust supervisory control in buildings

  • Model Predictive Control (MPC) for building energy management and indoor climate regulation has been an active research topic in the last decade. A plethora of research work has been published, while impressive results in real-world and simulated applications have been reported in relevant projects and publications. Despite the fact that MPC has evolved into a mature technology with tangible expected benefits, it has not found its way into the market. There are very few companies that include some simple aspects of MPC in their offerings – and still these implementations are far from being fully-fledged MPC solutions. This reluctance of companies to include MPC in their product lines cannot be attributed to any conservatism on the side of the industry; some of the mechanics of MPC are incompatible with the specific needs of building owners and operators. The biggest obstacle here is that MPC requires a model of the building, usually of a very specific linear or bi-linear structure. Model development and calibration induce the largestModel Predictive Control (MPC) for building energy management and indoor climate regulation has been an active research topic in the last decade. A plethora of research work has been published, while impressive results in real-world and simulated applications have been reported in relevant projects and publications. Despite the fact that MPC has evolved into a mature technology with tangible expected benefits, it has not found its way into the market. There are very few companies that include some simple aspects of MPC in their offerings – and still these implementations are far from being fully-fledged MPC solutions. This reluctance of companies to include MPC in their product lines cannot be attributed to any conservatism on the side of the industry; some of the mechanics of MPC are incompatible with the specific needs of building owners and operators. The biggest obstacle here is that MPC requires a model of the building, usually of a very specific linear or bi-linear structure. Model development and calibration induce the largest monetary costs in MPC application and in many cases can make the entire investment unattractive. Another important aspect is the inability of building managers to evaluate the performance of MPC and detect any errors in the setup in case of poor control configurations. Unlike traditional knowledge- based control in buildings where a controller consists of a set of (simple) rules, in MPC an optimization algorithm constantly designs new control strategies for the near future (usually in the form of time- series of setpoints for the building actuators), taking into account the available model of the building and forecasts. If poor control actions are generated, a large amount of information needs to be back- tracked – a task significantly more complex than examining the execution of simple control rules. Taking into account all the above, in the present work we define a methodology that borrows some of the ideas that enable the great potential of MPC, while at the same time satisfies the requirements of building owners on control robustness, explainable control decisions and low cost. To achieve this, in the heart of the methodology lies a data-driven approach for predicting the future thermal state of the building. So, instead of using purpose-built models for every study building, we utilize Gaussian Processes (GPs) to construct regression models based on historical data from the building. A Gaussian Process is a collection of random variables, any finite number of which has a joint Gaussian distribution and is completely specified by its mean and covariance functions. This means that the prediction of a GP for a specific future state is not a point estimate, but a distribution that reflects the predictive confidence of the model. This way, the optimization algorithm can avoid regions with high uncertainty, thus leading to more robust control. The second ingredient of our solution is that the optimized control strategies are not necessarily time- series of setpoints to be applied to the actuators; they can be optimized parameters of the knowledge- based controllers already running in the building. For example, if the supply water temperature of a radiant system is regulated using a heating curve with the external temperature as input, then the optimized controller of the proposed methodology would be a new heating curve, adapted e.g. to the weather predictions of the following day. This feature allows building managers to interpret the control actions applied to the building and increases their acceptability of the solution. The third aspect of the methodology is that instead of re-designing the control frequently (e.g. every hour) like in the classical MPC, we design and apply controllers for the entire day. This reduces significantly the computational complexity associated with classical online MPC, and makes our solution mode robust to connectivity problems or downtimes. In the work presented here, we evaluate the proposed approach at simulation level, using an EnergyPlus model of a real office building in Portugal. The building is equipped with a set of Variable Refrigerant Flow (VRF) systems for heating and for cooling and our solution optimizes the day-to-day operational cost (under a time-of-use tariff) without compromising occupant comfort, by controlling the heating setpoints in selected offices. At the same time, we provide evidence on the quality of the GP predictions on the problem at hand, as well as on the scalability of the proposed approach. On the other hand, two strong requirements of the methodology (similar to the requirements of classical system identification theory) became apparent from the experiments. First, high-quality historical data need to be available from the real building in order to train the GP models. And second, the available historical data may not capture the full model dynamics as e.g. the control setpoints of all offices might be set to a specific value and thus lack in input excitation. In this case, the proposed solution will be unable to discover optimized control strategies “far” from the training data.show moreshow less

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Author:Georgios KontesORCiD, Víctor Sánchez, Pablo de Agustin-CamachoORCiD, Borja Tellado, Georgios Giannakis, Simone Steiger, Gunnar Gruen, Dimitrios Rovas
URN:urn:nbn:de:bvb:92-opus4-4061
Place of publication:Nuremberg, Germany
Document Type:conference proceeding (article)
Language:English
Publishing Institution:Technische Hochschule Nürnberg Georg Simon Ohm
Release Date:2018/12/17
Tag:Data-driven control
Pagenumber:41
Note:
Funding:

1. H2020-EeB-2015: Modelling Optimization of Energy Efficiency
in Buildings for Urban Sustainability (MOEEBIUS, #680517)

2. H2020-EeB5-2015: Optimised Energy Efficient Design
Platform for Refurbishment at District Level (OptEEmAL, #680676)

3. Federal Ministry of Education and Research of Germany in the framework of Machine Learning Forum (grant number 01IS17071)
Source:Integration of Sustainable Energy EXPO & CONFERENCE, 17-18 July, 2018, Nuremberg, Germany
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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