Long-Term Electricity Demand Forecasting Using Relevance Vector Learning Mechanism-Reference-Cited by-同舟云学术

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Long-Term Electricity Demand Forecasting Using Relevance Vector Learning Mechanism

Published:2007 Issue: Volume: Page:465-472
ISSN:0302-9743
Container-title:Advances in Neural Networks – ISNN 2007
language:
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Author:

Du Zhi-gang,Niu Lin,Zhao Jian-guo

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-72383-7_55

Reference8 articles.

1. Vlahovic, V.M., Vujosevic, I.M.: Long-term Forecasting: a Critical Review of Direct-trend Extrapolation Methods. Electrical Power Energy Systems 9(1), 2–8 (1987)

2. Barakat, E.H., Al-Rashed, S.A.: Long Range Peak Demand Forecasting under Conditions of High Growth. Power Systems 7(4), 1483–1486 (1992)

3. Gen, M.R.: Electric Supply and Demand in the United States: Next 10 years. IEEE Power Eng. Rev., 8–13 (1992)

4. Leung, P.S., Miklius, W.: Accuracy of Electric Power Consumption Forecasts Generated by Alternative Methods: the Case of Hawaii. Energy Source 16, 289–299 (1994)

5. Chen, B.J.: Load Forecasting using Supporting Vector Machines: a Study on Eunite Competition 2001. Power Systems 19(4), 1821–1829 (2004)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Indian Peak Power demand Forecasting: Transformer Based Implementation of Temporal Architecture;2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT);2022-09-23

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