E3S Web Conf.
Volume 136, 20192019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019)
|Number of page(s)||4|
|Section||Ultra-Low Energy Consumption Building Technology|
|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: firstname.lastname@example.org
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
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