By Jörg Lässig, Kristian Kersting, Katharina Morik
The ebook to hand provides an outline of the cutting-edge study in Computational Sustainability in addition to case reports of other software eventualities. This covers subject matters similar to renewable power offer, power garage and e-mobility, potency in facts facilities and networks, sustainable foodstuff and water provide, sustainable overall healthiness, business construction and caliber, and so forth. The ebook describes computational tools and attainable software scenarios.
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Additional resources for Computational Sustainability
Meteocontrol—Energy and Weather Services GmbH. com 15. : Predicting solar radiation at high resolutions: a comparison of time series forecasts. Solar Energy 83(3), 342–349 (2009) 16. : Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA (2001) 17. : A tutorial on support vector regression. In: Statistics and Computing, vol. 14, pp. 199–222. Kluwer Academic Publishers, Hingham, MA, USA (2004) 18. : Statistical learning for short-term photovoltaic power predictions.
The growing IT infrastructure allows a fine screening of PV power. On the basis of big data sets of PV measurements, we apply methods from statistical learning for one- to six-hour ahead predictions based on data with hourly resolution. In this work, we employ nearest neighbor regression and support vector regression for PV power predictions based on measurements and numerical weather predictions. We put an emphasis on the analysis of feature combinations based on these two data sources. After optimizing the settings and comparing the employed statistical learning models, we build a hybrid predictor that uses forecasts of both employed models.
Instead, we dramatically lower the overhead with our new wind energy predictor: we construct weighted nearest-neighbor tables based on the two most correlated variables contributing to wind energy output: the wind speed and direction . The weighted tables show preference to the most recent results and allow the algorithm to adapt to gradual changes, while the power curves, based on both wind speed and direction, provide versatility. The algorithm to add a new entry to the table is in Eq. 2, where Pnew (v, d) is the new power curve table entry for a given wind velocity v and direction d, Pold (v, d) is the existing value, and Pobs (v, d, t) is the observed value at time t.