| Issue |
E3S Web Conf.
Volume 136, 2019
2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 4 | |
| Section | Ultra-Low Energy Consumption Building Technology | |
| DOI | https://doi.org/10.1051/e3sconf/201913601012 | |
| Published online | 10 December 2019 | |
Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant
1 State Grid Shandong Electric Power Research Institute, Jinan, Shandong, 250021, China
2 State Grid Zhangqiu Power Supply Company, Jinan, Shandong, 250001, China
* Corresponding author’s e-mail: lk83@163.com
A multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM（Adaptive moment）optimizer is selected to find the optimum parameters for the model during training. Under the proposed forecasting framework, experiments illustrates that the two-layer LSTM model can give a more accurate forecast to the condenser vacuum degree compared with other simple RNN (Recurrent Neural Network) and one-layer LSTM model.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.