Open Access
Issue |
E3S Web Conf.
Volume 256, 2021
2021 International Conference on Power System and Energy Internet (PoSEI2021)
|
|
---|---|---|
Article Number | 02032 | |
Number of page(s) | 6 | |
Section | Energy Internet R&D and Smart Energy Application | |
DOI | https://doi.org/10.1051/e3sconf/202125602032 | |
Published online | 10 May 2021 |
- Kummerow A., Klaiber S., Nicolai S., Bretschneider P., System A. (2015) Recursive analysis and forecast of superimposed generation and load time series. In: International ETG Congress 2015 Die Energiewende-Blueprints for the new energy age. Germany. pp. 198–203. [Google Scholar]
- Song K.B., Baek Y.S., Hong D.H., Jang G. (2005) Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Transactions on Power Systems, 20(1): 96–101. [Google Scholar]
- Christiaanse W.R. (1971) Short-term load forecasting using general exponential smoothing. IEEE Transactions on Power Apparatus and Systems, PAS-90(2): 900–911. [Google Scholar]
- Izadyar N., Ghadamian H., Ong H.C., Moghadam Z., Tong C.W., Shamshirband S. (2015) Appraisal of the support vector machine to forecast residential heating demand for the district heating system based on the monthly overall natural gas consumption. Energy, 93: 1558–1567. [Google Scholar]
- Yao Z.H., Xu X., Yu H.Q. (2018) Floor heating customer prediction model based on random forest. In: IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). Singapore. pp. 573–578. [Google Scholar]
- Wang J.Q., Du Y., and Wang J. (2020) LSTM based long-term energy consumption prediction with periodicity. Energy, 197. [Google Scholar]
- Powell K.M., Sriprasad A., Cole W.J., Edgar T.F. (2014) Heating, cooling, and electrical load forecasting for a large-scale district energy system. Energy, 74: 877–885. [Google Scholar]
- Tan Z.F., De G., Li M.L., Lin H.Y., Yang S.B., Huang L.L. Tan Q.K. (2020) Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine. Journal of Cleaner Production, 248. [Google Scholar]
- Tang Y., Liu H., Xie Y., Zhai J., Wu X. (2019) Short-term forecasting of electricity and gas demand in multi-energy system based on RBF-NN Model. In: IEEE International Conference on Energy Internet (ICEI). Nanjing. pp.542–547. [Google Scholar]
- Li K., Sun Y., Li S., Ma X., Zhang C. (2019) Load forecasting method for CCHP system based on deep learning strategy using LSTM-RNN. In: IEEE Conference on Industrial Electronics and Applications (ICIEA). Xi’an. pp.827–831. [Google Scholar]
- Deng D.Y., Li J., Zhang Z.Y., Qi H. (2020) Short-term electric load forecasting based on EEMD-GRU-MLR. Power System Technology, 44(2): 593–602. [Google Scholar]
- Sang H.F., Chen Z.Z. (2019) 3D human motion prediction based on bi-directional gated recurrent unit. Journal of Electronics & Information Technology, 41(9): 2256–2263. [Google Scholar]
- Rui Z., Zhao Y.D., Yan X., Ke M., Wong K.P. (2013) Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine. IIET Generation Transmission & Distribution, 7(4): 391–397. [Google Scholar]
- Powell K.M., Sriprasad A., Cole W.J., and Edgar T.F. (2014) Heating, cooling, and electrical load forecasting for a large-scale district energy system. Energy, 74: 877–885. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.