KRATKOROČNA PROGNOZA REŽIMA DISTRIBUTIVNE MREŽE
Ključne reči:
proračun prognoze, mašinsko učenje, teorija vektora podrške
Apstrakt
Rad treba da proveri tačnost rezultat algoritma teorije vektora podrške koji se koristi za proračun prognoze opterećenja izvoda distributivne mreže i pokaže da se tako dobijeni rezultati koriste za kalibraciju potrošnje koja pripada tom izvodu i pokrene proračun tokova snaga i indeksa performansi u cilju otkrivanja najkritičnijih elemenata u mreži za svaki prognozirani trenutak.
Reference
[1] Energetski bilans Republike Srbije za 2017. god.
[2] T.Hong: Short Term Electric Load Forecasting, North Carolina State University, 2010.
[3] M.M.Božić: Kratkoročna prognoza potrošnje električne energije zasnovana na metodama veštačke inteligencije, Univerzitet u Beogradu, Elektronski fakultet, Niš, 2014.
[4] N.Turker, F.Gunes: A competitive approach to neural device modeling: Support vector machines, Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, Vol. 4132. Springer, Berlin, Heidelberg
[5] B.E.Boser, I.Guyon, V.N.Vapnik: A training algorithm for optimal margin classifiers; In Computational Learning Theory, pp. 144-152, Pittsburgh, Pennsylvania, USA, July 27-29, 1992.
[6] F.Reossenblat: The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, Vol. 65, No.6, pp. 386-408, 1958.
[7] S.Kim, H.Kim: A new metric of absolute percentage error for intermittent demand forecasts, International Journal of Forecasting, Vol. 32, No. 3, pp. 669-679, 2016.
[2] T.Hong: Short Term Electric Load Forecasting, North Carolina State University, 2010.
[3] M.M.Božić: Kratkoročna prognoza potrošnje električne energije zasnovana na metodama veštačke inteligencije, Univerzitet u Beogradu, Elektronski fakultet, Niš, 2014.
[4] N.Turker, F.Gunes: A competitive approach to neural device modeling: Support vector machines, Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, Vol. 4132. Springer, Berlin, Heidelberg
[5] B.E.Boser, I.Guyon, V.N.Vapnik: A training algorithm for optimal margin classifiers; In Computational Learning Theory, pp. 144-152, Pittsburgh, Pennsylvania, USA, July 27-29, 1992.
[6] F.Reossenblat: The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, Vol. 65, No.6, pp. 386-408, 1958.
[7] S.Kim, H.Kim: A new metric of absolute percentage error for intermittent demand forecasts, International Journal of Forecasting, Vol. 32, No. 3, pp. 669-679, 2016.
Objavljeno
2019-11-03
Sekcija
Elektrotehničko i računarsko inženjerstvo
