Article
Wind Power Forecasting with Deep Learning Networks:
Time-Series Forecasting
†
Wen-Hui Lin
1
, Ping Wang
1,
* , Kuo-Ming Chao
2
, Hsiao-Chung Lin
1
, Zong-Yu Yang
1
and Yu-Huang Lai
1
Citation: Lin, W.-H.; Wang, P.; Chao,
K.-M.; Lin, H.-C.; Yang, Z.-Y.; Lai,
Y.-H. Wind Power Forecasting with
Deep Learning Networks:
Time-Series Forecasting. Appl. Sci.
2021, 11, 10335. https://doi.org/
10.3390/app112110335
Academic Editor: Nikos D. Lagaros
Received: 20 October 2021
Accepted: 31 October 2021
Published: 3 November 2021
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1
Green Energy Technology Research Center, Faculty of Department of Information Management,
Kun Shan University, Tainan 710303, Taiwan; linwh@mail.ksu.edu.tw (W.-H.L.);
fordlin@mail.ksu.edu.tw (H.-C.L.); s109000200@g.ksu.edu.tw (Z.-Y.Y.); s106001738@g.ksu.edu.tw (Y.-H.L.)
2
Engineering and Computing, School of MIS, Coventry University, Coventry CV1 5FB, UK;
csx240@coventry.ac.uk
* Correspondence: pingwang@mail.ksu.edu.tw; Tel.: +886-6-205-0545
† This paper is an extended version of our paper published in 7th IEEE International Conference on Applied
System Innovation 2021 (IEEE ICASI2021), Chiayi, Taiwan, 24–25 September 2021.
Abstract:
Studies have demonstrated that changes in the climate affect wind power forecasting
under different weather conditions. Theoretically, accurate prediction of both wind power output
and weather changes using statistics-based prediction models is difficult. In practice, traditional
machine learning models can perform long-term wind power forecasting with a mean absolute
percentage error (MAPE) of 10% to 17%, which does not meet the engineering requirements for
our renewable energy project. Deep learning networks (DLNs) have been employed to obtain
the correlations between meteorological features and power generation using a multilayer neural
convolutional architecture with gradient descent algorithms to minimize estimation errors. This
has wide applicability to the field of wind power forecasting. Therefore, this study aimed at the
long-term (24–72-h ahead) prediction of wind power with an MAPE of less than 10% by using the
Temporal Convolutional Network (TCN) algorithm of DLNs. In our experiment, we performed TCN
model pretraining using historical weather data and the power generation outputs of a wind turbine
from a Scada wind power plant in Turkey. The experimental results indicated an MAPE of 5.13%
for 72-h wind power prediction, which is adequate within the constraints of our project. Finally, we
compared the performance of four DLN-based prediction models for power forecasting, namely,
the TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence
unit (GRU) models. We validated that the TCN outperforms the other three models for wind power
prediction in terms of data input volume, stability of error reduction, and forecast accuracy.
Keywords:
renewable energy; wind power forecasting; deep learning network; temporal convolu-
tional network; long short-term memory
1. Introduction
With the increasingly serious global warming crisis and the burning of fossil fuels
inducing air pollution and climate change, concerned parties have begun to invest in the
development and application of renewable energy. European countries such as Denmark,
Germany, and Sweden have invested in renewable energy through smart power grids, in
which power suppliers and regional suppliers provide two-way complementary power
supply and demand. The key technology of a smart power grid is power forecasting in
relation to renewable energy, which is a clean power supply.
Many techniques have been applied to wind power forecasting to solve various
problems, such as the fluctuations in power from wind farms for very short-term, short-
term (from 30 min to day-ahead), medium-term (from day-ahead to month-ahead), and
long-term (more than month-ahead) [1].
Appl. Sci. 2021, 11, 10335. https://doi.org/10.3390/app112110335 https://www.mdpi.com/journal/applsci
