Download Computational Sustainability by Jörg Lässig, Kristian Kersting, Katharina Morik PDF

By Jörg Lässig, Kristian Kersting, Katharina Morik

The e-book to hand provides an summary of the state-of-the-art study in Computational Sustainability in addition to case stories of alternative software situations. This covers subject matters akin to renewable power offer, strength garage and e-mobility, potency in info facilities and networks, sustainable nutrition and water offer, sustainable future health, business creation and caliber, and so on. The booklet describes computational equipment and attainable program scenarios.

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Short-term wind energy forecasting using support vector regression. In: 6th International Conference on Soft Computing Models in Industrial and Environmental Applications (2011) 11. : Analysis of wind energy time series with kernel methods and neural networks. In: 7th International Conference on Natural Computation (2011) 12. : Wind power ramp event prediction with support vector machines. In: 9th International Conference on Hybrid Artificial Intelligence Systems (2014) 13. : A neural networks approach for wind speed prediction.

1, pp. 239–292. Springer (2012) 13. : Artificial intelligence technique for modelling and forecasting of solar radiation data—a review. Int. J. Artif. Intell. Soft Comput. 1(1), 52–76 (2008) 14. 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.

Wind Eng. Ind. Aerodyn. 90(3), 231–246 (2002) 5. : Learning wind fields with multiple kernels. Stoch. Env. Res. Risk Assess. 25(1), 51–66 (2011) 6. : Precise wind power prediction with SVM ensemble regression. In: Artificial Neural Networks and Machine Learning—ICANN 2014, pp. 797–804. Springer, Switzerland (2014) 7. : Neural network ensemble method study for wind power prediction. In: Asia Pacific Power and Energy Engineering Conference (APPEEC) (2011) 8. : Probabilistic short-term wind power forecasting based on kernel.

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