Open Access

ARTICLE

A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

1 NARI Information & Communication Technology Co., Ltd., Nanjing, 210008, China
2 School of Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China

* Corresponding Author: Hailong Wu. Email:

Computers, Materials & Continua 2024, 81(2), 3063-3077. https://doi.org/10.32604/cmc.2024.055381

Received 25 June 2024; Accepted 08 October 2024; Issue published 18 November 2024

Abstract

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.

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Keywords

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