Boiler Load Forecasting of CHP Plant Based on Attention Mechanism and Deep Neural Network

Abstract

Accurate boiler load forecasting of cogeneration units plays a direct role in production management and dispatching of power plants. A long-term load forecasting model of combined heat and power (CHP) based on attention mechanism and the deep convolution long-short-term memory network (CNN-LSTM-AM) is proposed, which takes the historical data of boiler outlet steam flow (load) and multi-dimensional load influence factors as input to make long-term load forecasting. First, the original data is screened by Pearson correlation coefficient judgment. Then the processed data is processed by convolution layer for feature extraction and further dimensionality reduction, fitted through long-term and short-term memory layer, and optimized the weight by adopting attention mechanism, so as to achieve accurate load forecasting. The proposed model is verified by the measured data of Tongxiang Power Plant in Zhejiang Province. The results show that the MAPE of the proposed method is less than 1%. It can realize the accurate prediction of boiler load, which has a certain reference significance for the application of intelligent algorithm in the field of combined heat and power.

Keywords

• combined heat and power (chp)
• attention mechanism (am)
• convolution neural network (cnn)
• long-short term memory (lstm)
• load forecasting