@article{KontesGiannakisHornetal.2017, author = {Kontes, Georgios and Giannakis, Georgios and Horn, Philip and Steiger, Simone and Rovas, Dimitrios}, title = {Using Thermostats for Indoor Climate Control in Office Buildings: The Effect on Thermal Comfort}, series = {Energies}, volume = {10}, journal = {Energies}, number = {9}, publisher = {MDPI AG}, address = {Basel, Switzerland}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-2465}, pages = {22 S.}, year = {2017}, abstract = {Thermostats are widely used in temperature regulation of indoor spaces and have a direct impact on energy use and occupant thermal comfort. Existing guidelines make recommendations for properly selecting set points to reduce energy use, but there is little or no information regarding the actual achieved thermal comfort of the occupants. While dry-bulb air temperature measured at the thermostat location is sometimes a good proxy, there is less understanding of whether thermal comfort targets are actually met. In this direction, we have defined an experimental Simulation protocol involving two office buildings; the buildings have contrasting geometrical and construction characteristics, as well as different building services systems for meeting heating and cooling demands. A parametric analysis is performed for combinations of controlled variables and boundary conditions. In all cases, occupant thermal comfort is estimated using the Fanger index, as defined in ISO 7730. The results of the parametric study suggest that simple bounds on the dry-bulb air temperature are not sufficient to ensure comfort, and in many cases, more detailed considerations taking into account building characteristics, as well as the types of building heating and cooling services are required. The implication is that the calculation or estimation of detailed comfort indices, or even the use of personalised comfort models, is key towards a more human-centric approach to building design and operation.}, subject = {Raumtemperatur}, language = {en} } @inproceedings{GiannakisKontesKorolijaetal.2017, author = {Giannakis, Georgios and Kontes, Georgios and Korolija, Ivan and Rovas, Dimitrios}, title = {Simulation-time Reduction Techniques for a Retrofit Planning Tool}, series = {Proceedings of Building Simulation 2017: 15th Conference of IBPSA}, booktitle = {Proceedings of Building Simulation 2017: 15th Conference of IBPSA}, editor = {Barnaby, Charles and Wetter, Michael}, isbn = {978-1-7750520-0-5}, issn = {2522-2708}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-2516}, pages = {S. 2014 - 2023}, year = {2017}, abstract = {The design of retrofitted energy efficient buildings is a promising option towards achieving a cost-effective improvement of the overall building sector's energy performance. With the aim of discovering the best design for a retrofitting project in an automatic manner, a decision making (or optimization) process is usually adopted, utilizing accurate building Simulation models towards evaluating the candidate retrofitting scenarios. A major factor which affects the Overall computational time of such a process is the simulation execution time. Since high complexity and prohibitive simulation execution time are predominantly due to the full-scale, detailed simulation, in this work, the following simulation-time reduction methodologies are evaluated with respect to accuracy and computational effort in a test building: Hierarchical clustering; Koopman modes; and Meta-models. The simplified model that would be the outcome of these approaches, can be utilized by any optimization approach to discover the best retrofitting option.}, language = {en} } @inproceedings{KontesRovas2016, author = {Kontes, Georgios and Rovas, Dimitrios}, title = {Modelling and prediction of buildings energy consumption using Machine Learning techniques}, series = {Integration of Sustainable Energy Conference (ISEneC 2016), 11 - 12 July 2016, Nuermberg, Germany}, booktitle = {Integration of Sustainable Energy Conference (ISEneC 2016), 11 - 12 July 2016, Nuermberg, Germany}, address = {Nuremberg, Germany}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-2527}, pages = {[32 S.]}, year = {2016}, abstract = {The ability to accurately forecast the building energy consumed can be valuable in a number of contexts. In Energy Performance Contracting and retrofitting actions, a key requirement for determining the Return on Investment is a precise and accurate quantification of the energy and concomitant cost savings resulting from the implementation of a set of Energy Conservation Measures. The precise quantification of energy savings is critical for Energy Savings Companies (ESCOs), under the guaranteed or shared savings contracting models; typically, this is done following a Measurement and Verification (M\&V) protocol where the pre-intervention energy, computed using a regression model is compared against actual post-intervention measured energy values. Another example, during the building operational phase, is the use of operational intelligence platforms to compare between expected and actual performance, and inform the facility manager when deviations or degradations are observed so that actions can be taken. Common to both these examples, is the need for a model capable of predicting with sufficient accuracy the energy consumption, while taking into account key factors that affect the overall performance, like prevailing weather conditions, occupancy patterns, etc. In the literature two classes of such models are often encountered: i) bottom-up, physics-based whole building simulation models; and ii) models based on statistical or machine learning techniques, requiring data typically obtained from the building monitoring system. The development of physics-based models necessitates a laborious and costly design and calibration process requiring expert knowledge; in the work presented here, data-driven models of the latter category are investigated since they have less stringent requirements, typically the availability of good-quality data. Different types of machine-learning models have been reported in literature, such as Neural Networks, Support Vector Machines, Gaussian Mixtures, etc. These are regression models aiming at identifying the underlying relationship between the depended variables (in our case the total energy consumption of the building) and the independent or explanatory variables, such as outside temperature levels, solar radiation, occupancy levels, etc. From the available model types, we adopt Gaussian Processes (GPs) since they do not require the excessive fine-tuning other models necessitate (e.g. selecting the number of hidden layers and nodes of a Neural Network or the hyper-parameters for the Support Vector Machines). GP models are purely data-oriented in the sense that an a-priori definition of a structural relationship between the dependent and explanatory variables is not required; what is required instead is the selection/specification of the covariance structure of the independent variables to explain the interaction with the dependent variable. An added benefit of GP regression models is that an estimate on the prediction uncertainty is also available. It has been shown that GPs require less data and lead to more accurate uncertainty estimates compared to standard regression methods, like the ones typically used in the application of the International Performance Measurement and Verification Protocol (IPMVP). The proposed methodology has been applied using measurements obtained from a study building. The task at hand is the modelling and prediction of both the daily and hourly total building energy consumption. At each time resolution, we have identified (different) suitable explanatory variables and performed a sensitivity analysis to evaluate the contribution of each variable on the final accuracy of the model. Two sets of data are used: training data for constructing the regression model; and test data for evaluating the accuracy of the prediction, utilizing common statistical indices such as Mean Squared Error (MSE) and coefficient of determination (R2) to evaluate the quality of the predictions. The experiments performed indicate the importance of selecting proper independent variables for each task, since the quality of the prediction largely depends on the ability of the explanatory variables to describe the behaviour of the dependent variable. In addition, the prediction quality and robustness of the GP regression was evident in all our tests, which combined with the laborious-free tuning of the framework make this approach highly-attractive for the task.}, language = {en} } @inproceedings{KontesSanchezdeAgustinCamachoetal.2018, author = {Kontes, Georgios and S{\´a}nchez, V{\´i}ctor and de Agustin-Camacho, Pablo and Tellado, Borja and Giannakis, Georgios and Steiger, Simone and Gruen, Gunnar and Rovas, Dimitrios}, title = {Data-driven approach for robust supervisory control in buildings}, address = {Nuremberg, Germany}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-4061}, pages = {41}, year = {2018}, abstract = {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 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.}, language = {en} } @article{KontesGiannakisSanchezetal.2018, author = {Kontes, Georgios and Giannakis, Georgios and S{\´a}nchez, V{\´i}ctor and de Agustin-Camacho, Pablo and Romero-Amorrortu, Ander and Panagiotidou, Natalia and Rovas, Dimitrios and Steiger, Simone and Mutschler, Christopher and Gruen, Gunnar}, title = {Simulation-Based Evaluation and Optimization of Control Strategies in Buildings}, volume = {11}, number = {12}, publisher = {MDPI AG}, address = {Basel, Switzerland}, doi = {10.3390/en11123376}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-4053}, pages = {23}, year = {2018}, abstract = {Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.}, language = {en} }