E3S Web Conf.
Volume 53, 20182018 3rd International Conference on Advances in Energy and Environment Research (ICAEER 2018)
|Number of page(s)||4|
|Section||Energy Equipment and Application|
|Published online||14 September 2018|
- Chen Y, Li P. Research on Simulation of Short-term Power Load Forecasting based on Neural Network. J. Electr. Eng, (2017). [Google Scholar]
- Chen Y, Xu P, Chu Y, et al. Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. J. Appl. Energy 195, 659-670 (2017). [CrossRef] [Google Scholar]
- Khwaja A S, Zhang X, Anpalagan A, et al. Boosted neural networks for improved short-term electric load forecasting.J. Electr Pow Syst Res, 143, 431-437 (2017). [CrossRef] [Google Scholar]
- Bi S T, Wang P F, Pan X N, et al. Understanding the dynamical mechanism of year-to-year incremental prediction by nonlinear time series prediction theory. J. Atmos. Ocean. Sci. Lett, 1, 1-7 (2018). [Google Scholar]
- Chen Y, Kloft M, Yang Y, et al. Mixed kernel based extreme learning machine for electric load forecasting. J. Neurocomputing, (2018). [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.