TY  - EJOU
AU  - Wu, Shaoxiong 
AU  - Li, Ruoxin 
AU  - Tao, Xiaofeng 
AU  - Wu, Hailong 
AU  - Miao, Ping 
AU  - Lu, Yang 
AU  - Lu, Yanyan 
AU  - Liu, Qi 
AU  - Pan, Li 

TI  - A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 2
SN  - 1546-2226

AB  - Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two parts: one is a stacked time convolutional memory unit module for global and local feature extraction, and the other is a residual combination optimization module to reduce model redundancy. Finally, this paper demonstrates through various experimental indicators that RST-LSTM achieves significant performance improvements in both overall and local prediction accuracy compared to some state-of-the-art baseline methods.
KW  - Times series forecasting; long short term memory network (LSTM); time convolutional network (TCN); wavelet decomposition

DO  - 10.32604/cmc.2024.055381