{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T13:04:13Z","timestamp":1749128653157},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2016,5,18]],"date-time":"2016-05-18T00:00:00Z","timestamp":1463529600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genet Program Evolvable Mach"],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1007\/s10710-016-9268-6","type":"journal-article","created":{"date-parts":[[2016,5,18]],"date-time":"2016-05-18T05:10:07Z","timestamp":1463548207000},"page":"391-408","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Dynamic feedback neuro-evolutionary networks for forecasting the highly fluctuating electrical loads"],"prefix":"10.1007","volume":"17","author":[{"given":"Gul Muhammad","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faheem","family":"Zafari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,5,18]]},"reference":[{"issue":"1","key":"9268_CR1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TEVC.2013.2285122","volume":"19","author":"GM Khan","year":"2015","unstructured":"G.M. Khan, R. Arshad, S.A. Mahmud, F. Ullah, Intelligent bandwidth estimation for variable bit rate traffic. IEEE Trans. Evol. Comput. 19(1), 151\u2013155 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"9268_CR2","first-page":"17","volume":"9","author":"C Kadilar","year":"2009","unstructured":"C. Kadilar, M. Simsek, C.H. Aladag, Forecasting the exchange rate series with ann: the case of Turkey. Istanb. Univ. Economet. Stat J. 9(1), 17\u201329 (2009)","journal-title":"Istanb. Univ. Economet. Stat J."},{"key":"9268_CR3","doi-asserted-by":"crossref","unstructured":"E. El-Attar, J. Goulermas, Q. Wu, Forecasting electric daily peak load based on local prediction, in Power & Energy Society General Meeting, 2009 (PES\u201909) (IEEE, 2009), pp.\u00a01\u20136","DOI":"10.1109\/PES.2009.5275587"},{"key":"9268_CR4","doi-asserted-by":"crossref","unstructured":"G.M. Khan, F. Zafari, S.A. Mahmud, Very short term load forecasting using Cartesian genetic programming evolved recurrent neural networks (cgprnn), in 12th International Conference on Machine Learning and Applications (ICMLA), vol.\u00a02, (IEEE, 2013), pp.\u00a0152\u2013155","DOI":"10.1109\/ICMLA.2013.181"},{"key":"9268_CR5","unstructured":"M.M. Khan, G.M. Khan, J.F. Miller, Evolution of optimal ANNs for non-linear control problems using Cartesian genetic programming, in Proceedings of the 2010 International Conference on Artificial Intelligence Intelligence (IC-AI 2010), July 12\u201315, 2010, Las Vegas, NV, pp. 339\u2013346"},{"key":"9268_CR6","doi-asserted-by":"crossref","unstructured":"F. Zhao, H. Su, Short-term load forecasting using Kalman filter and elman neural network, in 2nd IEEE Conference on Industrial Electronics and Applications (ICIEA) (IEEE, 2007), pp.\u00a01043\u20131047","DOI":"10.1109\/ICIEA.2007.4318567"},{"issue":"2","key":"9268_CR7","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1049\/ip-gtd:20050088","volume":"153","author":"H Al-Hamadi","year":"2006","unstructured":"H. Al-Hamadi, S. Soliman, Fuzzy short-term electric load forecasting using Kalman filter. IEE Proc. Gener. Transm. Distrib. 153(2), 217\u2013227 (2006)","journal-title":"IEE Proc. Gener. Transm. Distrib."},{"key":"9268_CR8","doi-asserted-by":"crossref","unstructured":"J.-H. Lim, O.-S. Kwon, K.-B. Song, J.-D. Park, Short-term load forecasting for educational buildings with temperature correlation, in Fourth International Conference on Power Engineering, Energy and Electrical Drives (POWERENG) (IEEE, 2013), pp.\u00a0405\u2013408","DOI":"10.1109\/PowerEng.2013.6635641"},{"key":"9268_CR9","doi-asserted-by":"crossref","unstructured":"S. Ramos, J. Soares, Z. Vale, Short-term load forecasting based on load profiling, in Power and Energy Society General Meeting (PES) (IEEE, 2013), pp.\u00a01\u20135","DOI":"10.1109\/PESMG.2013.6672439"},{"issue":"4","key":"9268_CR10","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1109\/TPWRS.2004.835679","volume":"19","author":"B-J Chen","year":"2004","unstructured":"B.-J. Chen, M.-W. Chang, C.-J. Lin, Load forecasting using support vector machines: a study on eunite competition 2001. IEEE Trans. Power Syst. 19(4), 1821\u20131830 (2004)","journal-title":"IEEE Trans. Power Syst."},{"issue":"3","key":"9268_CR11","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.epsr.2005.01.006","volume":"74","author":"P-F Pai","year":"2005","unstructured":"P.-F. Pai, W.-C. Hong, Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr. Power Syst. Res. 74(3), 417\u2013425 (2005)","journal-title":"Electr. Power Syst. Res."},{"issue":"1","key":"9268_CR12","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TSG.2013.2274373","volume":"5","author":"T Hong","year":"2014","unstructured":"T. Hong, J. Wilson, J. Xie, Long term probabilistic load forecasting and normalization with hourly information. IEEE Trans. Smart Grid 5(1), 456\u2013462 (2014)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"4","key":"9268_CR13","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1109\/TPWRS.2014.2298463","volume":"29","author":"X Chen","year":"2014","unstructured":"X. Chen, C. Kang, X. Tong, Q. Xia, J. Yang, Improving the accuracy of bus load forecasting by a two-stage bad data identification method. IEEE Trans. Power Syst. 29(4), 1634\u20131641 (2014)","journal-title":"IEEE Trans. Power Syst."},{"issue":"1","key":"9268_CR14","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.ijepes.2012.09.002","volume":"45","author":"R-A Hooshmand","year":"2013","unstructured":"R.-A. Hooshmand, H. Amooshahi, M. Parastegari, A hybrid intelligent algorithm based short-term load forecasting approach. Int. J. Electr. Power Energy Syst. 45(1), 313\u2013324 (2013)","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"6","key":"9268_CR15","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.ijepes.2005.12.007","volume":"28","author":"P Mandal","year":"2006","unstructured":"P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, A neural network based several-hour-ahead electric load forecasting using similar days approach. Int. J. Electr. Power Energy Syst. 28(6), 367\u2013373 (2006)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"9268_CR16","doi-asserted-by":"crossref","unstructured":"A.K. Pandey, K.B. Sahay, M. Tripathi, D. Chandra, Short-term load forecasting of uppcl using ann, in 6th IEEE Power India International Conference (PIICON) (IEEE, 2014), pp.\u00a01\u20136","DOI":"10.1109\/POWERI.2014.7117741"},{"key":"9268_CR17","doi-asserted-by":"crossref","unstructured":"K.B. Sahay, N. Kumar, M. Tripathi, Short-term load forecasting of ontario electricity market by considering the effect of temperature, in 6th IEEE Power India International Conference (PIICON) (IEEE, 2014), pp.\u00a01\u20136","DOI":"10.1109\/POWERI.2014.7117756"},{"issue":"9","key":"9268_CR18","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1109\/5.784219","volume":"87","author":"X Yao","year":"1999","unstructured":"X. Yao, Evolving artificial neural networks. Proc. IEEE 87(9), 1423\u20131447 (1999)","journal-title":"Proc. IEEE"},{"key":"9268_CR19","first-page":"937","volume":"9","author":"F Gomez","year":"2008","unstructured":"F. Gomez, J. Schmidhuber, R. Miikkulainen, Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res. 9, 937\u2013965 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"9268_CR20","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"K.O. Stanley, R. Miikkulainen, Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99\u2013127 (2002)","journal-title":"Evol. Comput."},{"issue":"6","key":"9268_CR21","first-page":"1359","volume":"29","author":"Z Va\u0161\u00ed\u010dek","year":"2012","unstructured":"Z. Va\u0161\u00ed\u010dek, L. Sekanina, Hardware accelerator of Cartesian genetic programming with multiple fitness units. Comput. Inform. 29(6), 1359\u20131371 (2012)","journal-title":"Comput. Inform."},{"key":"9268_CR22","unstructured":"J.A. Rothermich, J.F. Miller, Studying the emergence of multicellularity with Cartesian genetic programming in artificial life, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Late Breaking Papers (Morgan Kaufmann Publishers, 2002), pp.\u00a0397\u2013403"},{"key":"9268_CR23","doi-asserted-by":"crossref","unstructured":"M. Akole, B. Tyagi, Artificial neural network based short term load forecasting for restructured power system, in\u00a0International Conference on Power Systems, 2009 (ICPS\u201909) (IEEE, 2009), pp.\u00a01\u20137","DOI":"10.1109\/ICPWS.2009.5442781"},{"issue":"1","key":"9268_CR24","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/TPWRS.2011.2162082","volume":"27","author":"S Fan","year":"2012","unstructured":"S. Fan, R.J. Hyndman, Short-term load forecasting based on a semi-parametric additive model. IEEE Trans. Power Syst. 27(1), 134\u2013141 (2012)","journal-title":"IEEE Trans. Power Syst."}],"container-title":["Genetic Programming and Evolvable Machines"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-016-9268-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10710-016-9268-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-016-9268-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-016-9268-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,7]],"date-time":"2019-09-07T19:47:11Z","timestamp":1567885631000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10710-016-9268-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,5,18]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,12]]}},"alternative-id":["9268"],"URL":"https:\/\/doi.org\/10.1007\/s10710-016-9268-6","relation":{},"ISSN":["1389-2576","1573-7632"],"issn-type":[{"value":"1389-2576","type":"print"},{"value":"1573-7632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,5,18]]}}}