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Input Convex Neural Networks for building MPC
Bünning, F., Schalbetter, A., Aboudonia, A., Hudoba de Badyn, M., Heer, P., & Lygeros, J. (2021). Input Convex Neural Networks for building MPC. In A. Jadbabaie, J. Lygeros, G. J. Pappas, P. A. Parrilo, B. Recht, C. J. Tomlin, & M. N. Zeilinger (Eds.), Proceedings of machine learning research: Vol. 144. Learning for dynamics and control (pp. 251-262). ML Research Press.
Experiment strategy for evaluating advanced building energy management system
Cai, H., Khayatian, F., & Heer, P. (2021). Experiment strategy for evaluating advanced building energy management system. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 2042. CISBAT 2021 carbon neutral cities - energy efficiency & renewables in the digital era (p. 012030 (6 pp.). https://doi.org/10.1088/1742-6596/2042/1/012030
Experimental implementation of a context-aware prosumer
Cai, H., & Heer, P. (2021). Experimental implementation of a context-aware prosumer. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 2042. CISBAT 2021 carbon neutral cities - energy efficiency & renewables in the digital era (p. 012068 (6 pp.). https://doi.org/10.1088/1742-6596/2042/1/012068
Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies
Di Natale, L., Svetozarevic, B., Heer, P., & Jones, C. N. (2021). Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 2042. CISBAT 2021 carbon neutral cities - energy efficiency & renewables in the digital era (p. 012004 (6 pp.). https://doi.org/10.1088/1742-6596/2042/1/012004
The potential of vehicle-to-grid to support the energy transition: a case study on Switzerland
Di Natale, L., Funk, L., Rüdisüli, M., Svetozarevic, B., Pareschi, G., Heer, P., & Sansavini, G. (2021). The potential of vehicle-to-grid to support the energy transition: a case study on Switzerland. Energies, 14(16), 4812 (24 pp.). https://doi.org/10.3390/en14164812
Multi-objective optimization of a power-to-hydrogen system for mobility via two-stage stochastic programming
Fochesato, M., Heer, P., & Lygeros, J. (2021). Multi-objective optimization of a power-to-hydrogen system for mobility via two-stage stochastic programming. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 2042. CISBAT 2021 carbon neutral cities - energy efficiency & renewables in the digital era (p. 012034 (6 pp.). https://doi.org/10.1088/1742-6596/2042/1/012034
Predictive energy management of residential buildings while self-reporting flexibility envelope
Gasser, J., Cai, H., Karagiannopoulos, S., Heer, P., & Hug, G. (2021). Predictive energy management of residential buildings while self-reporting flexibility envelope. Applied Energy, 288, 116653 (14 pp.). https://doi.org/10.1016/j.apenergy.2021.116653
Benchmarking of data predictive control in a real-life apartment during heating season
Huber, B., Felix, B., Antoon, D., Heer, P., Aboudonia, A., & Lygeros, J. (2021). Benchmarking of data predictive control in a real-life apartment during heating season. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 2042. CISBAT 2021 carbon neutral cities - energy efficiency & renewables in the digital era (p. 012024 (6 pp.). https://doi.org/10.1088/1742-6596/2042/1/012024
Handbuch zur Entwicklung erneuerbarer dezentraler Energiesysteme
Mennel, S., Sulzer, M., Yilmaz, S., Patel, M., Chambers, J., Knoeri, C., … Sulzer Worlitschek, S. (2021). Handbuch zur Entwicklung erneuerbarer dezentraler Energiesysteme. SCCER FEEB&D; Hochschule Luzern.
Benchmarking cooling and heating energy demands considering climate change, population growth and cooling device uptake
Mutschler, R., Rüdisüli, M., Heer, P., & Eggimann, S. (2021). Benchmarking cooling and heating energy demands considering climate change, population growth and cooling device uptake. Applied Energy, 288, 116636 (11 pp.). https://doi.org/10.1016/j.apenergy.2021.116636
Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments
Svetozarevic, B., Baumann, C., Muntwiler, S., Di Natale, L., Zeilinger, M. N., & Heer, P. (2021). Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments. Applied Energy. https://doi.org/10.1016/j.apenergy.2021.118127
ReMaP: Forschungsplattform für Multienergiesysteme
Boulouchos, K., Brenzikofer, A., Demiray, T., von Euw, M., Flamm, B., Haselbacher, A., … Ulbig, A. (2020). ReMaP: Forschungsplattform für Multienergiesysteme. Aqua & Gas, 100(9), 14-21.
Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings
Bünning, F., Huber, B., Heer, P., Aboudonia, A., & Lygeros, J. (2020). Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings. Energy and Buildings, 211, 109792 (8 pp.). https://doi.org/10.1016/j.enbuild.2020.109792
Frequency regulation with heat pumps using robust MPC with affine policies
Bünning, F., Warrington, J., Heer, P., Smith, R. S., & Lygeros, J. (2020). Frequency regulation with heat pumps using robust MPC with affine policies. In R. Findeisen, S. Hirche, K. Janschek, & M. Mönnigmann (Eds.), IFAC-PapersOnline: Vol. 53. 21st IFAC World Congress 2020 (pp. 13210-13215). https://doi.org/10.1016/j.ifacol.2020.12.147
Improved day ahead heating demand forecasting by online correction methods
Bünning, F., Heer, P., Smith, R. S., & Lygeros, J. (2020). Improved day ahead heating demand forecasting by online correction methods. Energy and Buildings, 211, 109821 (13 pp.). https://doi.org/10.1016/j.enbuild.2020.109821
Characterization of heat-pump, PV and battery demonstrator technologies using a coherent energy assessment
Allan, J., Georges, G., Heer, P., Budiman, D., & Croce, L. (2019). Characterization of heat-pump, PV and battery demonstrator technologies using a coherent energy assessment. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 1343. CISBAT 2019 international conference on climate resilient cities - energy efficiency & renewables in the digital era (p. 012105 (6 pp.). https://doi.org/10.1088/1742-6596/1343/1/012105
Empirical validation of a data-driven heating demand simulation with error correction methods
Bünning, F., Bollinger, A., Heer, P., Smith, R. S., & Lygeros, J. (2019). Empirical validation of a data-driven heating demand simulation with error correction methods. In V. Corrado, E. Fabrizio, A. Gasparella, & F. Patuzzi (Eds.), Proceedings of the international building performance simulation association: Vol. 16. Proceedings of building simulation 2019: 16th conference of IBPSA (pp. 1428-1435). https://doi.org/10.26868/25222708.2019.210673
Sensitivity analysis of data-driven building energy demand forecasts
Bünning, F., Heer, P., Smith, R. S., & Lygeros, J. (2019). Sensitivity analysis of data-driven building energy demand forecasts. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 1343. CISBAT 2019 international conference on climate resilient cities - energy efficiency & renewables in the digital era (p. 012062 (6 pp.). https://doi.org/10.1088/1742-6596/1343/1/012062
Controller tuning by Bayesian optimization. An application to a heat pump
Khosravi, M., Eichler, A., Schmid, N., Heer, P., & Smith, R. S. (2019). Controller tuning by Bayesian optimization. An application to a heat pump. In 2019 18th European control conference (ECC) (pp. 1467-1472). https://doi.org/10.23919/ECC.2019.8795801
Machine learning-based modeling and controller tuning of a heat pump
Khosravi, M., Schmid, N., Eichler, A., Heer, P., & Smith, R. S. (2019). Machine learning-based modeling and controller tuning of a heat pump. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 1343. CISBAT 2019 international conference on climate resilient cities - energy efficiency & renewables in the digital era (p. 012065 (6 p.). https://doi.org/10.1088/1742-6596/1343/1/012065