Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations
Behrens G, Schlender K, Brandt M, Kösling P (2019)
In: Environmental Informatics: Computational Sustainability. Schaldach R, Simon K-H, Weismüller J, Wohlgemuth V (Eds); Skaker: 381 ff.
Konferenzbeitrag | Englisch
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Autor*in
Behrens, Grit;
Schlender, Klaus;
Brandt, MaraUniBi 
;
Kösling, Philipp
;
Kösling, PhilippHerausgeber*in
Schaldach, Rüdiger;
Simon, Karl-Heinz;
Weismüller, Jens;
Wohlgemuth, Volker
Einrichtung
Erscheinungsjahr
2019
Titel des Konferenzbandes
Environmental Informatics: Computational Sustainability
Seite(n)
381 ff.
Konferenz
EnviroInfo 2019
Konferenzort
Kassel
Konferenzdatum
2019-09-23 – 2019-09-26
ISBN
978-3-8440-6847-4
Page URI
https://pub.uni-bielefeld.de/record/2960149
Zitieren
Behrens G, Schlender K, Brandt M, Kösling P. Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations. In: Schaldach R, Simon K-H, Weismüller J, Wohlgemuth V, eds. Environmental Informatics: Computational Sustainability. Skaker; 2019: 381 ff.
Behrens, G., Schlender, K., Brandt, M., & Kösling, P. (2019). Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations. In R. Schaldach, K. - H. Simon, J. Weismüller, & V. Wohlgemuth (Eds.), Environmental Informatics: Computational Sustainability (p. 381 ff.). Skaker.
Behrens, Grit, Schlender, Klaus, Brandt, Mara, and Kösling, Philipp. 2019. “Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations”. In Environmental Informatics: Computational Sustainability, ed. Rüdiger Schaldach, Karl-Heinz Simon, Jens Weismüller, and Volker Wohlgemuth, 381 ff. Skaker.
Behrens, G., Schlender, K., Brandt, M., and Kösling, P. (2019). “Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations” in Environmental Informatics: Computational Sustainability, Schaldach, R., Simon, K. - H., Weismüller, J., and Wohlgemuth, V. eds. (Skaker), 381 ff.
Behrens, G., et al., 2019. Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations. In R. Schaldach, et al., eds. Environmental Informatics: Computational Sustainability. Skaker, pp. 381 ff.
G. Behrens, et al., “Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations”, Environmental Informatics: Computational Sustainability, R. Schaldach, et al., eds., Skaker, 2019, pp.381 ff.
Behrens, G., Schlender, K., Brandt, M., Kösling, P.: Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations. In: Schaldach, R., Simon, K.-H., Weismüller, J., and Wohlgemuth, V. (eds.) Environmental Informatics: Computational Sustainability. p. 381 ff. Skaker (2019).
Behrens, Grit, Schlender, Klaus, Brandt, Mara, and Kösling, Philipp. “Supervised machine learning of environmental energy consumption types by AI algorithms targeting CO2 emission reduction and avoidance of bad air quality by giving recommendations”. Environmental Informatics: Computational Sustainability. Ed. Rüdiger Schaldach, Karl-Heinz Simon, Jens Weismüller, and Volker Wohlgemuth. Skaker, 2019. 381 ff.
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