Citation: Liu, S.; Ning, D.; Ma, J.
TCNformer Model for Photovoltaic
Power Prediction. Appl. Sci. 2023, 13,
2593. https://doi.org/10.3390/
app13042593
Academic Editor: Sergio
Nesmachnow
Received: 7 February 2023
Revised: 15 February 2023
Accepted: 16 February 2023
Published: 17 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
TCNformer Model for Photovoltaic Power Prediction
Shipeng Liu
1,2
, Dejun Ning
1,
* and Jue Ma
1,2
1
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 200120, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
* Correspondence: ningdj@sari.ac.cn; Tel.: +86-186-1653-6063
Abstract:
Despite the growing capabilities of the short-term prediction of photovoltaic power, we still
face two challenges to longer time-range predictions: error accumulation and long-term time series
feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic
power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-
of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series
feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS
module employs correlation analysis and periodic analysis to separate the time series correlation
information, LSTFE extracts multiple time series features from time series data, and one-step TCN
decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean
squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in
contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and
furthermore, the effectiveness of each module has been verified through ablation experiments.
Keywords:
transformer; SkipGRU; TCN; photovoltaic power prediction; time series data prediction
1. Introduction
At present, with the rapid development of perovskite solar cell technology [
1
,
2
], the
maximum efficiency [
3
] and stability [
4
] of photovoltaic power have been greatly improved.
Photovoltaic power is increasingly important in the field of new energy. According to
the data of the International Energy Agency (IEA), the growth rate of global photovoltaic
installed capacity has reached as much as 49%. It is estimated that global photovoltaic
power will reach 16% of the total power in 2050 [
5
]. At the same time, China is promoting
the construction of a new power system with new energy as the principal part. Photovoltaic
power using solar energy is an important branch of new energy and one of the important
means for China to achieve the goal of carbon neutrality. After the large-scale integration
of photovoltaic power stations into the energy network, the manner by which to accurately
predict photovoltaic power and then accordingly dispatch the power grid has become
an urgent problem to be addressed. Therefore, improving the prediction accuracy of
photovoltaic power is significant for improving the operation efficiency of power stations
themselves and for maintaining the stability of power grids.
Many scholars in China and abroad have carried out a lot of research on the prediction
of photovoltaic power. At present, the mainstream prediction methods focus on traditional
random learning and deep learning methods. In the field of traditional random learning,
literature [
6
] uses historical weather data and historical power data as inputs of a support
vector machine (SVM) to build a short-term photovoltaic power prediction model, which
has a higher level of accuracy than the traditional autoregressive model (AR) or the radial
basis function (RBF) models. One study [
7
] proposed a model based on Support Vector
Regression (SVR) and achieved better prediction performance. In the field of deep learning,
recurrent neural network (RNN) structures, such as long short-term memory (LSTM), gated
recurrent unit (GRU), and seq2seq structural models, are widely used to analyze and predict
time series data for such applications as stock price prediction [
8
], gold price prediction [
9
],
Appl. Sci. 2023, 13, 2593. https://doi.org/10.3390/app13042593 https://www.mdpi.com/journal/applsci
