Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System
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- Jacobsson, Staffan & Lauber, Volkmar, 2006. "The politics and policy of energy system transformation--explaining the German diffusion of renewable energy technology," Energy Policy, Elsevier, vol. 34(3), pages 256-276, February.
- Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
- Karoline A. Mester & Marion Christ & Melanie Degel & Wolf-Dieter Bunke, 2017. "Integrating Social Acceptance of Electricity Grid Expansion into Energy System Modeling: A Methodological Approach for Germany," Progress in IS, in: Volker Wohlgemuth & Frank Fuchs-Kittowski & Jochen Wittmann (ed.), Advances and New Trends in Environmental Informatics, pages 115-129, Springer.
- McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
- Esen, Hikmet & Inalli, Mustafa & Esen, Yuksel, 2009. "Temperature distributions in boreholes of a vertical ground-coupled heat pump system," Renewable Energy, Elsevier, vol. 34(12), pages 2672-2679.
- Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
- Žáčeková, Eva & Váňa, Zdeněk & Cigler, Jiří, 2014. "Towards the real-life implementation of MPC for an office building: Identification issues," Applied Energy, Elsevier, vol. 135(C), pages 53-62.
- Verzijlbergh, R.A. & De Vries, L.J. & Dijkema, G.P.J. & Herder, P.M., 2017. "Institutional challenges caused by the integration of renewable energy sources in the European electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 660-667.
- Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
- Wang, Guang Chao & Ratnam, Elizabeth & Haghi, Hamed Valizadeh & Kleissl, Jan, 2019. "Corrective receding horizon EV charge scheduling using short-term solar forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 1146-1158.
- Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
- Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
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Keywords
data-based modeling; data-driven modeling; least-squares regression; linear regression; clustering; simulated annealing; nonlinear optimization; self-consumption optimization; energy management;All these keywords.
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