{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:49:51Z","timestamp":1740098991161,"version":"3.37.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811071782"},{"type":"electronic","value":"9789811071799"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-981-10-7179-9_40","type":"book-chapter","created":{"date-parts":[[2017,11,7]],"date-time":"2017-11-07T23:53:32Z","timestamp":1510098812000},"page":"511-522","source":"Crossref","is-referenced-by-count":3,"title":["Effect of Transfer Functions in Deep Belief Network for Short-Term Load Forecasting"],"prefix":"10.1007","author":[{"given":"Xiaoyu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabin","family":"Zha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,11,9]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Amjady, N.: Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans. Power. Syst. 49805 (2001)","DOI":"10.1109\/59.962429"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Fan, S., Chen, L.: Short-term load forecasting based on an adaptive hybrid method. IEEE Trans. Power. Syst. 798\u2013805 (2006)","DOI":"10.1109\/TPWRS.2005.860944"},{"key":"40_CR3","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1016\/j.egypro.2012.01.229","volume":"16","author":"H Nie","year":"2012","unstructured":"Nie, H., Liu, G., Liu, X., Wang, Y.: Hybrid of ARIMA and SVMs for short-term load forecasting. Energy Procedia 16, 1455\u20131460 (2012)","journal-title":"Energy Procedia"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Amral, N., Ozveren, C.S., King, D.: Short term load forecasting using multiple linear regression. In: 42nd International Universities Power Engineering Conference 2007, pp. 1192\u20131198. IEEE (2007)","DOI":"10.1109\/UPEC.2007.4469121"},{"key":"40_CR5","unstructured":"Zhang, M., Bao, H., Yan, L., Cao, J., Du, J.: Research on processing of short-term historical data of daily load based on Kalman filter. Power Syst. Tech. 39\u201342 (2003)"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhen-gang, Z.: Based on time sequence of arima model in the application of short-term electricity load forecasting. In: International Conference on Research Challenges in Computer Science, pp. 11\u201314 (2009)","DOI":"10.1109\/ICRCCS.2009.12"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Irisarri, G.D., Widergren, S.E., Yehsakul, P.D.: On-Line Load Forecasting for Energy Control Center Application. IEEE Trans. Power Appar. Syst. 71\u201378 (1982)","DOI":"10.1109\/TPAS.1982.317242"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Christiaanse, W.R.: Short-term load forecasting using general exponential smoothing. IEEE Trans. Power Appar. Syst. 900\u2013911 (2007)","DOI":"10.1109\/TPAS.1971.293123"},{"key":"40_CR9","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/S0196-8904(02)00148-6","volume":"44","author":"K Metaxiotis","year":"2003","unstructured":"Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J.: Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy Convers. Manag. 44, 1525\u20131534 (2003)","journal-title":"Energy Convers. Manag."},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Kouhi, S., Keynia, F.: A new cascade NN based method to short-term load forecast in deregulated electricity market. Energy Convers. Manag. 76\u201383 (2013)","DOI":"10.1016\/j.enconman.2013.03.014"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yan, W.: Short-term load forecasting of power systems by combination of wavelet transform and AMPSO based neural network, Energy Procedia 46\u201357 (2011)","DOI":"10.1016\/j.egypro.2011.12.265"},{"key":"40_CR12","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1016\/j.enconman.2007.09.009","volume":"49","author":"P Lauret","year":"2008","unstructured":"Lauret, P., Fock, E., Randrianarivony, R.N., Manicom-Ramsamy, J.F.: Bayesian neural network approach to short time load forecasting. Energy Convers. Manag. 49, 1156\u20131166 (2008)","journal-title":"Energy Convers. Manag."},{"key":"40_CR13","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1109\/59.99372","volume":"5","author":"KL Ho","year":"1990","unstructured":"Ho, K.L., Hsu, Y.Y., Chen, C.F., Lee, T.E., Liang, C.C., Lai, T.S.: Short term load forecasting of Taiwan power system using a knowledge-based expert system. IEEE Trans. Power. Syst. 5, 1214\u20131221 (1990)","journal-title":"IEEE Trans. Power. Syst."},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"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. 1558\u20131573 (2002)","DOI":"10.1109\/TPWRS.2002.1007923"},{"key":"40_CR15","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504\u2013507 (2006)","journal-title":"Science"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Chao, J., Shen, F., Zhao, J.: Forecasting exchange rate with deep belief networks. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2011), San Jose, California, USA, pp. 1259\u20131266 (2011)","DOI":"10.1109\/IJCNN.2011.6033368"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, R., Zhang, T., et al.: Short-term load forecasting based on a improved deep belief network. In: International Conference on Smart Grid and Clean Energy Technologies, pp. 339\u2013342 (2016)","DOI":"10.1109\/ICSGCE.2016.7876080"},{"key":"40_CR18","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2000","unstructured":"Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural. Comput. 18, 1527\u20131554 (2000)","journal-title":"Neural. Comput."}],"container-title":["Communications in Computer and Information Science","Bio-inspired Computing: Theories and Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-10-7179-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,5]],"date-time":"2019-10-05T16:00:07Z","timestamp":1570291207000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-10-7179-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9789811071782","9789811071799"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-10-7179-9_40","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2017]]}}}