As one of the countries with most energy consumption in world, electricity accounts for a large proportion supply our country. According to national basic policy conservation and emission reduction, it is urgent realize intelligent distribution management by prediction. Due complex nature load sequences, traditional model predicts poor results. kernel-based machine learning model, Gaussian Proc...

The shaft deflection is one of critical factors that deteriorate the grease lubrication state inside the tapered roller bearings (TRBs). So, in this paper, on the premise of the TRB subjected to the combined loads and the shaft is deflected, the grease lubrication model at the TRB contacts was constructed, in which the interaction loads, linear and angular displacements of the bearing parts wer...

Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving accuracy electricity is vital for public resources management departments. Traditional neural network such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in forecasting. However, its variants are not sensitive...

In an effort to address the load adjustment time in thermal and electrical distribution of power plant units, we propose optimal method based on prediction among multiple units plants. The proposed utilizes optimization by attention fine-tune a deep convolutional long-short-term memory network (CNN-LSTM-A) model for accurately predicting heat supply two 30 MW extraction back pressure units. Fir...