, , ,


 >>  ()
Pages:     | 1 | 2 ||


-- [ 3 ] --

118. Antonio Piras. These "A Multiresponse structural connectionist model for short term electrical load forecasting".- Lausanne. EPFL, 1996. P.


119. Bakirtzis A.G., Petridis V., Klartzis S. J.,Alexiadis M.C. A neural network short term load forecasting model for the Greek power system // IEEE Transactions on Power Systems, Vol. 11, No.2, May 1998, P. 858863.

120. Barakat E.H. Modeling of nonstationary time-series data. P. I, II // Electrical Power and Energy Syst. 2001. 23. P. 57-68.

www.elsevier.com/locate/ijepes 121. Brierley P.O., Batty W.J. Electric load modelling with neural net woarks: An Insight into the Black Box //Proc. ICONIP-97. Dunedin. 1997. V. 2.

P. 1326-1329.

122. C. Rodriguez, S. Rementeria, J.I. Martin, A. Lafuente, J. Muguerza, J. Perez. Fault analysis with modular neural networks. //Electrical Power & En ergy Systems, 1996. Vol. 18.-2.-P. 99-110.

123. Chang-il Kim, In-keun Yu, Song Y.H. Kohonen neural network and wavelet transform based approach to short-term load forecasting // Electric Power Syst. Res. 2002. 63. P. 169-176. www.elsevier.com/locate/epsr 124. Che-Chiang Hsu, Chia-Yon Chen. Regional load forecasting in Tai wan-applications of artificial neural networks // Energy Conversion and Man agement. 2002.www.elsevier.com/locate/enconman 125. Clements K.A., Krumpholz G.R., Davis P.W. Power system state es timation residual analysis: an algorithm using network topology // IEEE Trans.

PAS, 1984. 4. P. 1779-1787.

126. Corchado J.M., Fyfe C. Unsupervised neural method for temperature forecasting // Artificial Intelligence in Engineering. 1999. 13. P. 351-357.

127. Dash P.K., Satpathy H.P., Liew A.C. A real-time short-term peak and average load forecasting system using a self-organising fuzzy neural net work // Engin. Applic. of Artificial Intelligence. 1998. 11. P. 307-316.

128. Dotzauer E. Simple model for prediction of loads in district-heating systems // Appl. Energy 2002. 73. P. 277-284.

www.elsevier.com/locate/apenergy 129. Fay D., Ringwood J.V., Condon M., Kelly M. 24-h electrical load data-sequential or partitioned time series // Neurocomputing. 2003.


www.elsevier.com/locate/neu-com 130. Hopfield J., Tank D. Neural Computation of Decisions in Optimiza tion Problems // Biological Cybernetics. 1981. v. 52.

131. Huang H.-C., Hwang R.-C., Hsieh J.-G. A new artificial intelligent peak power load forecasting based on non-fixed neural networks // Electrical Power and Energy Syst. 2002. 24. P. 245-250.

www.elsevier.com/locate/ijepes 132. J..S. Souza, A.M. Leite da Silva, A. P. Alves da Silva. Data visuali sation and identification of anomalies in power system state estimation using ar tificial neural networks // IEE Ppoc. Sep. 1997. Vol. 144. -5. -P. 445-455.

133. J. C. S. Souza, A. M. Leite da Silva, A. P. Alves da Silva. Online to pology determination and bad data suppression in power system operation using artificial neural networks. // IEEE Transactions on Power Systems, August 1998. Vol.13. - 3.-P.796-803.

134. Kalaitzakis K., Stavrakakis G.S., Anagnostakis EM. Short-term load forecasting based on artificial neural networks parallel implementation // Elec tric Power Syst. Res. 2002. 63. P. 185-196. www.elsevier.com/locate/epsr 135. Kanevski M., Demyanov V., Maignan M. Spatial estimations and simulations of environmental data by using geostatistics and artificial neural network // Proc. IAMG-97. Barcelona. 1997. P. 533-538.

136. Kermanshahi ., Iwaminya H. Up to year 2020 load forecasting us ing neural nets // Electrical Power and Energy Syst. 2002. 24. P. 789-797.

www.elsevier.com/locate/ijepes 137. Kodogiannis VS., Anagnostakis EM. Soft computing based tech niques for short-term load forecasting // Fuzzy Sets and Systems. 2002. 128.

P. 413-426. www.elsevier.com/locate/fss 138. Kogin H.J., Neisius TH., Beibler G., Schmitt K.D., Bad data detec tion and identification. // Int. J. Electr. Power Energy Syst., 1990. -4(1 ). P.94-103.

139. Kohonen T. SelfOrganization and Associative Memory.- Berlin:

SpringnerVerlag. 1988.

140. Kohonen T. Self-organizing maps Berlin, Heidelberg: Springer Verlag, 1995. P. 141. Kryukov A.V., Raevskij N.V., Jakovlev D.A. Forecasting of the en ergy consumption with application of neuron net-works // Abstracts of the Inter national conference 29-31 March 2004, Irkutsk. Irkutsk: Irkutsk state transport university-Technological educational institution of Athens.- Irkutsk, 2004. P.79.

142. M. Kezunouit, I. Rikalo. Automating the Analysis of Faults Power Quality. //I Computer Application in Power, 1999. - P.42-47.

143. Metaxiotis K., Kagiannas A., Askounis D., Psarras J. Artificial intel ligence in short term electric load forecasting: a state-of-the-art survey for the researcher // Energy Conversion and Management. 2003. 44. P. 1525-1534.

www.elsevier.com/locate/enconman 144. Mohamed E.A., Mansour MM., El-Debeiky S., Mohamed K.G.

Egyptian unified grid hourly load forecasting using artifical neural network // Electrical Power and Energy Systems. 1998. V. 20. 7. P. 495-500.

145. Mount T. Market power and price volatility in restructured markets for electricity // Decision Support Syst. 2001. 30. P. 311-325.

www.elsevier.com/locate/erdsw 146. Nowicka-ZagrajekJ., Weron R. Modeling electricity loads in Cali fornia: ARMA models with hyperbolic noise // Signal Processing. 2002. 82.

P. 1903-1915. www.elsevier.com/locate/sigpro 147. Peng T. M., Hubele N.F., Karady G.G. An adaptive neural network approach to oneweek ahead load forecasting // IEEE Transactions on Power Systems, Vol. 8, No.3, August 1993, P. 11951197.

148. Piras A., Germond A., Buchenel B., Imhof K, Jaccard Y. Heteroge neous artificial neural network for term electrical load forecasting // IEEE Transactions on Power Systems, Vol. 11, No.1, February 1996, P. 397401.

149. Piras A. A multiresponse structural connectionist model for short term electrical load forecasting // These 1546. Lausanne. EPEL. 1996, P.177.

150. Principles of forecasting: a handbook for researchers and practitio ners. Norwell, MA: Kluwer Academic Publish. 1999.

151. Rojas R. Neural networks. A systematic introduction. Berlin:

Springer-Verlag, 1996.

152. Rosenblatt F. Principles of neurodynamics: perseptrons and the the ory of brain mechanisms. Spartans books/Washington D.C., 1962.

153. Savelieva E., Kravetski A., Chernov S. etal. Application of MLP and stochastic simulations for electricity load forecasting in Russia // Proc. ESANN 2000. Bruges. Belgium. 2000.

154. Schuhler E. Apport de la geostatistique aux modeles probabilistes de la finance // These pour obtenir le grade de Docteur de la science. Ecole de Mines de Paris, 12 octobre 1998.

155. Sforna M. NEUFOR: a tool for the online short-term load forecasting correction // Electric Power Systems Researhc. 1998. 48. P. 25-30.

www.elsevier.com/locate/epsr 156. Singh S.P., Mailk O.P. Single ANN architecture for short-term load forecasting for all seasons // Eng. Int. Syst. 1995. V. 3. 4. P. 249-254.

157. Tamimi M., Egbert R. Short term electric load forecasting via fuzzy neural collaboration // Electric Power Syst. Res. 2000. 56. P. 243-248.

www.elsevier.com/locate/epsr 158. Taylor J.W., Buizza R. Using weather ensemble predictions in elec tricity demand forecasting // Intern. J. Forecasting. 2003. 19. P. 57-70.

www.elsevier.com/locate/ijforecast 159. Topalli A.K., Erkmen I. A hybrid learning for neural networks ap plied to short-term load forecasting // Neurocomputing. 2003. 51. P. 495-500.

www.elsevier.com/locate/neucom 160. Verona F.B., Ceraolo M. Use of neural networks for customer tariff exploitation by means of short-term load forecasting // Neurocomputing. 1998.

23. P. 135-149.

161. Yao S.J., Song Y.H., Zhang L.Z., Cheng X.Y. Wavelet transform and neural networks for short-term electrical load forecasting // Energy Conver sion and Management. 2000. 41. P. 1975-1988.


Pages:     | 1 | 2 ||
 >>  ()


<<     |    
2013 www.libed.ru - -

, .
, , , , 1-2 .