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We introduce the Python-based open-source library Energym, a building model library to test and benchmark building controllers. The incorporated building models are presented with a brief explanation of their function, location and technical equipment. Furthermore, the library structure is described, highlighting the necessary features to provide the benchmarking and control capabilities, i.e., standardized evaluation scenarios, key performance indicators (KPIs) and forecasts of uncertain variables. We go on to characterize the evaluation scenarios for each of the models and give formal definitions of the KPIs. We describe the calibration methodologies used for constructing the models and illustrate their usage through examples.
Paul Scharnhorst; Baptiste Schubnel; Carlos Fernández Bandera; Jaume Salom; Paolo Taddeo; Max Boegli; Tomasz Gorecki; Yves Stauffer; Antonis Peppas; Chrysa Politi. Energym: A Building Model Library for Controller Benchmarking. Applied Sciences 2021, 11, 3518 .
AMA StylePaul Scharnhorst, Baptiste Schubnel, Carlos Fernández Bandera, Jaume Salom, Paolo Taddeo, Max Boegli, Tomasz Gorecki, Yves Stauffer, Antonis Peppas, Chrysa Politi. Energym: A Building Model Library for Controller Benchmarking. Applied Sciences. 2021; 11 (8):3518.
Chicago/Turabian StylePaul Scharnhorst; Baptiste Schubnel; Carlos Fernández Bandera; Jaume Salom; Paolo Taddeo; Max Boegli; Tomasz Gorecki; Yves Stauffer; Antonis Peppas; Chrysa Politi. 2021. "Energym: A Building Model Library for Controller Benchmarking." Applied Sciences 11, no. 8: 3518.
Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management. In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition that PV systems provide a dense network of simple weather stations. These methods rely entirely on production data and address the real-life challenges that come with them, such as noise and gaps. Our approach builds on graph signal processing for signal reconstruction and for forecasting with a linear, spatio-temporal autoregressive (ST-AR) model. It also introduces a data-driven clear-sky production estimation for normalization. The proposed framework was evaluated over one year on both 303 real PV systems under commercial monitoring across Switzerland, and 1000 simulated ones based on high-resolution weather data. The results demonstrate the performance and robustness of the approach: with gaps of four hours on average in the input data, the average daytime NRMSE over a six-hour forecasting horizon (in 15 min steps) and over all systems is 13.8% and 9% for the real and synthetic data sets, respectively.
Rafael E. Carrillo; Martin Leblanc; Baptiste Schubnel; Renaud Langou; Cyril Topfel; Pierre-Jean Alet. High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution. Energies 2020, 13, 5763 .
AMA StyleRafael E. Carrillo, Martin Leblanc, Baptiste Schubnel, Renaud Langou, Cyril Topfel, Pierre-Jean Alet. High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution. Energies. 2020; 13 (21):5763.
Chicago/Turabian StyleRafael E. Carrillo; Martin Leblanc; Baptiste Schubnel; Renaud Langou; Cyril Topfel; Pierre-Jean Alet. 2020. "High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution." Energies 13, no. 21: 5763.
Power consumption in buildings show nonlinear behaviours that linear models cannot capture, whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternatives for the model-predictive control (MPC) of buildings. However, RNNs are nonlinear and non-smooth functions which makes their use challenging in optimization problems. Therefore, this work systematically investigates whether using RNNs for building control provides net gains in MPC. It compares over 2 months of simulated operation the representation power and control performance of two architectures: an RNN architecture and a linear state-space (LSS) model with a nonlinear regressor to estimate energy consumption. The results show that RNNs yield an identification error 69% lower than LSS, but the LSS models yield control laws that achieve 10% lower objective function with a computational time three times lower than the RNNs. Thus, on balance, well-designed LSS models with nonlinear regressors are best in most cases of MPC.
Baptiste Schubnel; Rafael E. Carrillo; Paolo Taddeo; Lluc Canal Casals; Jaume Salom; Yves Stauffer; Pierre-Jean Alet. State-space models for building control: how deep should you go? Journal of Building Performance Simulation 2020, 13, 707 -719.
AMA StyleBaptiste Schubnel, Rafael E. Carrillo, Paolo Taddeo, Lluc Canal Casals, Jaume Salom, Yves Stauffer, Pierre-Jean Alet. State-space models for building control: how deep should you go? Journal of Building Performance Simulation. 2020; 13 (6):707-719.
Chicago/Turabian StyleBaptiste Schubnel; Rafael E. Carrillo; Paolo Taddeo; Lluc Canal Casals; Jaume Salom; Yves Stauffer; Pierre-Jean Alet. 2020. "State-space models for building control: how deep should you go?" Journal of Building Performance Simulation 13, no. 6: 707-719.
The electricity sector foresees a significant change in the way energy is generated and distributed in the coming years. With the increasing penetration of renewable energy sources, smart algorithms can determine the difference about how and when energy is produced or consumed by residential districts. However, managing and implementing energy demand response, in particular energy flexibility activations, in real case studies still presents issues to be solved. This study, within the framework of the European project “SABINA H2020”, addresses the development of a multi-level optimization algorithm that has been tested in a semi-virtual real-time configuration. Results from a two-day test show the potential of building’s flexibility and highlight its complexity. Results show how the first level algorithm goal to reduce the energy injected to the grid is accomplished as well as the energy consumption shift from nighttime to daytime hours. As conclusion, the study demonstrates the feasibility of such kind of configurations and puts the basis for real test site implementation.
Paolo Taddeo; Alba Colet; Rafael E. Carrillo; Lluc Casals Canals; Baptiste Schubnel; Yves Stauffer; Ivan Bellanco; Cristina Corchero Garcia; Jaume Salom. Management and Activation of Energy Flexibility at Building and Market Level: A Residential Case Study. Energies 2020, 13, 1188 .
AMA StylePaolo Taddeo, Alba Colet, Rafael E. Carrillo, Lluc Casals Canals, Baptiste Schubnel, Yves Stauffer, Ivan Bellanco, Cristina Corchero Garcia, Jaume Salom. Management and Activation of Energy Flexibility at Building and Market Level: A Residential Case Study. Energies. 2020; 13 (5):1188.
Chicago/Turabian StylePaolo Taddeo; Alba Colet; Rafael E. Carrillo; Lluc Casals Canals; Baptiste Schubnel; Yves Stauffer; Ivan Bellanco; Cristina Corchero Garcia; Jaume Salom. 2020. "Management and Activation of Energy Flexibility at Building and Market Level: A Residential Case Study." Energies 13, no. 5: 1188.
Baptiste Schubnel; Rafael E. Carrillo; Pierre-Jean Alet; Andreas Hutter. A Hybrid Learning Method for System Identification and Optimal Control. IEEE Transactions on Neural Networks and Learning Systems 2020, 1 -15.
AMA StyleBaptiste Schubnel, Rafael E. Carrillo, Pierre-Jean Alet, Andreas Hutter. A Hybrid Learning Method for System Identification and Optimal Control. IEEE Transactions on Neural Networks and Learning Systems. 2020; ():1-15.
Chicago/Turabian StyleBaptiste Schubnel; Rafael E. Carrillo; Pierre-Jean Alet; Andreas Hutter. 2020. "A Hybrid Learning Method for System Identification and Optimal Control." IEEE Transactions on Neural Networks and Learning Systems , no. : 1-15.