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  • (-) Organizational Unit = 313 Urban Energy Systems
  • (-) Publication Year = 2020 - 2020
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A moment and sum-of-squares extension of dual dynamic programming with application to nonlinear energy storage problems
Hohmann, M., Warrington, J., & Lygeros, J. (2020). A moment and sum-of-squares extension of dual dynamic programming with application to nonlinear energy storage problems. European Journal of Operational Research, 283(1), 16-32. https://doi.org/10.1016/j.ejor.2019.10.041
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
How weather affects energy demand variability in the transition towards sustainable heating
Eggimann, S., Usher, W., Eyre, N., & Hall, J. W. (2020). How weather affects energy demand variability in the transition towards sustainable heating. Energy, 195, 116947 (11 pp.). https://doi.org/10.1016/j.energy.2020.116947
Improved day ahead heating demand forecasting by online correction methods
Bnning, F., Heer, P., Smith, R. S., & Lygeros, J. (2020). Improved day ahead heating demand forecasting by online correction methods. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2020.109821
Introducing reinforcement learning to the energy system design process
Perera, A. T. D., Wickramasinghe, P. U., Nik, V. M., & Scartezzini, J. L. (2020). Introducing reinforcement learning to the energy system design process. Applied Energy, 262, 114580 (14 pp.). https://doi.org/10.1016/j.apenergy.2020.114580
Optimal transformation strategies for buildings, neighbourhoods and districts to reach CO<sub>2</sub> emission reduction targets
Murray, P., Marquant, J., Niffeler, M., Mavromatidis, G., & Orehounig, K. (2020). Optimal transformation strategies for buildings, neighbourhoods and districts to reach CO2 emission reduction targets. Energy and Buildings, 207, 109569 (34 pp.). https://doi.org/10.1016/j.enbuild.2019.109569
SHED Swiss Hub for Energy Data. Teil einer nationalen Energie-Dateninfrastruktur und Wegbereiter für Energiestrategie 2050, Klimaschutz und digitale Innovationen
Werlen, K., Petry, J., & Sulzer, M. (2020). SHED Swiss Hub for Energy Data. Teil einer nationalen Energie-Dateninfrastruktur und Wegbereiter für Energiestrategie 2050, Klimaschutz und digitale Innovationen (Report No.: SI/501857-01). sine loco: sine nomine.