Czasopismo
2019
|
R. 95, nr 7
|
155--159
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Medium-term load forecasting models - a review
Języki publikacji
Abstrakty
W artykule dokonano przeglądu metod i modeli prognostycznych dedykowanych średnioterminowemu prognozowaniu obciążeń elektroenergetycznych. Opisano metody modelowania warunkowego i autonomicznego, modele klasyczne, modele inteligencji obliczeniowej i uczenia maszynowego oraz modele oparte na podobieństwie obrazów.
The article reviews the methods and models of the medium-term load forecasting. Methods of conditional and autonomous modeling, classic models, computational intelligence and machine learning models are described, as well as pattern similarity-based models.
Czasopismo
Rocznik
Tom
Strony
155--159
Opis fizyczny
Bibliogr. 41 poz.
Twórcy
autor
- Politechnika Częstochowska, Instytut Informatyki, al. Armii Krajowej 17, 42-200 Częstochowa, p.pelka@el.pcz.czest.pl
Bibliografia
- [1] Piotrowski P.: Prognozowanie w elektroenergetyce w różnych horyzontach czasowych. Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa 2013
- [2] Mbamalu G.A.N., El -Hawary M.E.: Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation, IEEE Trans Power Systems, 8 (1993), nr 1, 343–7
- [3] Charytoniuk W. , Chen M.S.: Very short-term load forecasting using artificial neural networks. IEEE Trans Power Systems 15 (2000), nr 1, 263–8
- [4] Abdel-Aal R.E., Al-Garni A.Z.: Forecasting monthly electric energy consumption in Eastern Saudi Arabia using univariate time-series analysis. Energy, 22 (1997), nr 11, 1059–69.
- [5] Barakat E.H.: Modeling of nonstationary time-series data. Part II. Dynamic periodic trends. Electr Power Energy Systems 23 (2001), 63–8
- [6] Barakat E.H. , Al -Qasem J.M.: Methodology for weekly load forecasting. IEEE Trans. Power System 13 (1998), nr 4, 1548–55
- [7] Saab S., Badr E., Nasr G.: Univariate modeling and forecasting of Energy consumption: the case of electricity in Lebanon. Energy 26 (2001), nr 1, 1–14
- [8] Ringwood J.V., Bofelli D., Murray F.T.: Forecasting electricity demand on short, medium and long time scales using neural networks. J Intel Robot Systems 31 (2001), 129–47
- [9] Ghiassi M., Zimbra D. K., Saidane H.: Medium term system load forecasting with a dynamic artificial neural network model, Electric Power Systems Research 76 (2006), 302–316
- [10] Dudek G.: Analiza podobieństwa obrazów sekwencji szeregów czasowych obciążeń elektroenergetycznych, Przegląd Elektrotechniczny 85 (2009), nr 3, 149-152
- [11] Dudek G.: Systemy uczące się oparte na podobieństwie obrazów do prognozowania szeregów czasowych obciążeń elektroenergetycznych, Akademicka Oficyna Wydawnicza EXIT, Warszawa 2012
- [12] Gavrilas M., Ciutea I., Tanasa C.: Medium-term load forecasting with artificial neural network models, 16th International Conference and Exhibition on Electricity Distribution, 2001, EEE Conf. Elec. Dist. Pub. No. 482, 383.
- [13] González-Romera E., Jaramillo-Morán M. A, Carmona-Fernández D.: Monthly electric energy demand forecasting with neural networks and Fourier series, Energy Conversion and Management 49 (2008), 3135–3142.
- [14] Kandil M.S., El-Debeiky S.M., Hasanien N.E.: Long-term load forecasting for fast developing utility using a knowledge-based expert system, IEEE Trans. Power Syst. 17 (2002), nr 2, 491–496
- [15] Barakat E.H., Al -Rashed S.A.: Long range peak demand forecasting under condition of high growth, IEEE Trans. Power Syst. 7 (1992), nr 4, 1483–1486.
- [16] Islam S.M., Al-Alawi M., Ellithy K.A.: Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network, Electric Power Syst. Res., 34 (1995), 1–9
- [17] Bunnoon P., Chalermyanont K., Limsakul C.: Mid Term Load Forecasting of the Country Using Statistical Methodology: Case study in Thailand, 2009 International Conference on Signal Processing Systems, 924-928
- [18] Cai G., Yang D., Jiao Y. , Pan C.: The Characteristic Analysis and Forecasting of Mid-Long Term Load Based on Spatial Autoregressive Model, Proc. 2009 International Conference on Sustainable Power Generation and Supply, Nanjing, China
- [19] Chen B.J., Chang M.W., Lin C.J.: Load forecasting using support vector machines: a study on EUNITE competition 2001, IEEE Trans Power Systems 19 (2004), nr 4, 1821–30
- [20] Ranaweera D.K., Karady G.G., Farmer R.G.: Economic impact analysis of load forecasting, IEEE Trans. Power Syst. 12 (1997), nr 3, 1388–1392
- [21] Elkateb M. M., Solaiman K., Al-Turki Y. : A comparative study of medium-weather-dependent load forecasting using enhanced artificial/fuzzy neural network and statistical techniques, Neurocomputing 23 (1998), 3–13
- [22] Doveh E. , Feigin P. , Hyams L.: Experience with FNN models for medium term power demand predictions, IEEE Trans. Power Syst., 14 (1999), nr 2, 538-546
- [23] Pei-Chann C., Chin-Yuan F., Jyun-Jie L.: Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach, Electrical Power and Energy Systems, 33 (2011), 17–27
- [24] Al -Hamadi H.M. , Soliman S.A. : Long-term/mid-term electric load forecasting based on short-term correlation and annual growth, Electric Power Syst. Res., 74 (2005), 353–361.
- [25] Zagdański A., Suchwałko A.: Analiza i prognozowanie szeregów czasowych. Praktyczne wprowadzenie na podstawie środowiska R, Wydawnictwo naukowe PWN SA, Warszawa 2016
- [26] Dong-Liang Z., Yanjian, Wei-Hua W., Xiu-Lan Y. : Mid-long term load forecasting of the unstable growth sequence based on Markov chains screening combination forecasting models, 2016 China International Conference on Electricity Distribution (CICED 2016), Xi’an, 10-13 Aug, 2016
- [27] Peng T.M., Hubele N.F., Karadi G.G.: Advancement in the application of neural networks for short-term load forecasting. IEEE Trans Power Systems, 7 (1992), nr. 1, 250–7
- [28] Park D.C., El-Sharkawi M.A., Marks II R.J., Atlas L.E. , Damborg M. J .: Electric load forecasting using an artificial neural network. IEEE Trans Power Systems 6 (1991), nr. 2, 442–9
- [29] Padmakumari K., Mohandas K.P., Thiruvengadam S.: Long term distribution demand forecasting using neuro fuzzy computations. Electr Power Energy Systems, 21 (1999), 315–22
- [30] Gao R., Tsoulakas L.H.: Neural-wavelet methodology for load forecasting. J Intel Robot Systems 31 (2001), 149–57
- [31] González-Romera E., Jaramillo-Morán M.A., Carmona-Fernández D.: Monthly electric energy demand forecasting based on trend extraction. IEEE Trans. Power System, 21 (2006), nr 4,1935–46
- [32] Chen J.F., Lo S.K., Do Q.H.: Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained byHeuristic Algorithms, Information, 31 (2017), nr 8
- [33] Aquinode R. R. B, Neto O. N., Lira M. M. S., Ferreira A. A., Carvalho Jr. M.A., Silva G. B., Oliveirade J. B.: Development of an Artificial Neural Network by Genetic Algorithm to Mid-Term Load Forecasting, Proceedings of 2007 International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17, 2007
- [34] Borlea I ., Buta A., Lustrea B.: Some Aspects Concerning Mid Term Monthly Load Forecasting Using ANN, EUROCON 2005 - The International Conference on "Computer as a Tool", Belgrade, November 22-24, 2005
- [35] Zhao W., Wang F., Niu D. : The Application of Support Vector Machine in Load Forecasting, JOURNAL OF COMPUTERS, 7 (2012), nr. 7, 1615-1622
- [36] Sket Motnikar B., Pisanski T., Cepar D.: Timeseries forecasting by pattern imitation, OR Spektrum, Springer- Verlag, 18 (1996), 43-49
- [37] Singh S. , Stuart E. : A Pattern Matching Tool for Time- Series Forecasting, Proc. Fourteenth International Conference on Pattern Recognition, 20-20 Aug. 1998
- [38] Dudek G., Pełka P.: Forecasting monthly electricity demand using k nearest neighbor method, Przegląd Elektrotechniczny, 93 (2017) ,nr.4, 62-65.
- [39] Pełka P. , Dudek G.: Prediction of Monthly Electric Energy Consumption using Pattern-Based Fuzzy Nearest Neighbour Regression. Proc. 2nd Int. Conf. Computational Methods in Engineering Science (CMES'17), ITM Web Conf., 15 (2017), 1-5
- [40] Dudek G., Pełka P.: Medium-term electric energy demand forecasting using Nadaraya-Watson estimator. Proc. 18th Int. Scientific Conf. on Electric Power Engineering 2017 (EPE'17), 1-6
- [41] Pełka P., Dudek G.: Neuro-Fuzzy System for Medium-term Electric Energy Demand Forecasting. In: Borzemski L., Świątek J., Wilimowska Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017, Advances in Intelligent Systems and Computing, Springer, Cham, 655 (2018), 38-47
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-bbe3b58e-9f71-4ae3-94d5-9396ff4d946e