SHORT TERM FORECASTING OF THE DISTRIBUTION NETWORK STATE
DOI:
https://doi.org/10.24867/05BE20JerkovicKeywords:
load forecast, machine learning, support vector machineAbstract
The work needs to verify the accuracy of the result of the support vector machine algorithm used to calculate the load forecast of the distribution network and show that the results thus obtained are used to calibrate the consumption of that statement and trigger the load flow and performance index calculation in order to detect the most critical elements in the network for each forecasted moment.
References
[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.
Downloads
Published
2019-11-03
Issue
Section
Electrotechnical and Computer Engineering
