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|
Research on short-term electric load forecasting based on extreme learning machine
Department of Economic Management, North China Electric Power University, 071003 Baoding, China
* Corresponding author: firstname.lastname@example.org
As an important support for the development of the national economy, the power industry plays a role in ensuring economic operations. Time series prediction can process dynamic data, is widely used in economics and engineering, and especially is of great practical value in using historical data to predict future development. Under the guidance of extreme learning machine and time series theory, this paper applies the extreme learning machine to the study of time series, and builds a model for load forecasting research. Load forecasting plays an important role in power planning, affecting planning operation modes, power exchange schemes, etc., so load forecasting is very necessary in power planning. First, establish an extreme learning machine model; second, the short-term load forecasting is performed by different activation functions to verify the performance of the activation function.1 After empirical analysis, the activation function with the best predictive ability is obtained.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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