Sådan kan AI hjælpe med at omstille hele verden til vedvarende energi

Med kunstig intelligens kan den rette placering af og den optimale produktion fra vedvarende energikilder bestemmes. AI er derfor et væsentligt værktøj i en omstilling til fornybar energi, mener stifter af AI Academy.

Gianluca Mauro, CEO og stifter af AI Academy, AI-Academy

https://pro.ing.dk/739

4. sep 2018 05:59

Mange politiske, økonomiske og miljømæssige faktorer er overbevisende argumenter for at reducere forbruget af fossile brændsler. Som der står i en rapport fra IPCC (Intergovernmental Panel on Climate Change) i 2014 [1], så er den miljøsituation, vi lige nu er i, bekymrende og “overordentligt sandsynligt” forårsaget af menneskelig indblanding:

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Kilder

[1]: IPCC. Summary for Policymakers. 2014. ISBN 9789291691432. Doi: 10.1017/CBO9781107415324.

[2] D. Bunn and E.D. Farmer. Comparative models for electrical load forecasting. John Wiley and Sons Inc.,New York, NY, Jan 1985.

[3] Mohamed Ahmed. Short term wind speed estimation in Saudi Arabia. 128:37–53, 2014. doi: 10.1016/j.jweia.2014.02.007.

[4] Shafiqur Rehman and Mohamed Mohandes. Artificial neural network estimation of global solar radiation using air temperature and relative humidity. 36:571–576, 2008. doi: 10.1016/j.enpol.2007.09.033.

[5] A Azadeh, A Maghsoudi, and S Sohrabkhani. An integrated artificial neural networks approach for predicting global radiation. Energy Conversion and Management, 50(6):1497–1505, 2009. ISSN 01968904. doi: 10.1016/j.enconman.2009.02.019. URL http://dx.doi.org/10.1016/j.enconman.2009.02.019.

[6] M A Behrang, E Assareh, A Ghanbarzadeh, and A R Noghrehabadi. The potential of different artificial neural network ( ANN ) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy, 84(8):1468–1480, 2010. ISSN 0038–092X. doi: 10.1016/j. solener.2010.05.009. URL http://dx.doi.org/10.1016/j.solener.2010.05. 009.

[7] Betul Bektas Ekici. A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems. MEASUREMENT, 50:255–262, 2014. ISSN 0263–2241. doi: 10.1016/j.measurement.2014.01.010. URL http://dx.doi.org/10.1016/j.measurement.2014.01.010.

[8] Sozen Adnan, Arcaklioglu Erol, Mehmet Ozalpa, and E. Galip Kanit. Use of artificial neural networks for mapping of solar potential in Turkey. 77:273–286, 2004. doi: 10.1016/S0306–2619(03)00137–5.

[9] S. M. AI-Alawi AI-Hinai and H. A. An ANN-Based Approach for Predicting Global Radiation in Locations with No Direct Measurement Instrumentation . 14:199–204, 1998.

[10] K S Reddy and Manish Ranjan. Solar resource estimation using artificial neural networks and comparison with other correlation models. 44:2519–2530, 2003. doi: 10.1016/S0196–8904(03)00009–8.

[11] Soteris A. Kalogirou. Optimization of solar systems using artificial neural-networks and genetic algorithms. Applied Energy, 2004. ISSN 03062619. doi: 10.1016/S0306–2619(03)00153–3.

[12] Aoife M Foley, Paul G Leahy, Antonino Marvuglia, and Eamon J Mckeogh. Current methods and advances in forecasting of wind power generation. Renewable Energy, 37(1):1–8, 2012. ISSN 0960–1481. doi: 10.1016/j.renene.2011.05.033. URL http://dx.doi.org/10.1016/j. renene.2011.05.033.

[13] Mohamed A Mohandes and Shafiqur Rehman. A Neural Networks Approach for Wind Speed Prediction. pages 345–354, 1998.

Sådan kan AI hjælpe med at omstille hele verden til vedvarende energi

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