Citation: Shi, Y.; Wang, Y.; Zheng, H.
Wind Speed Prediction for Offshore
Sites Using a Clockwork Recurrent
Network. Energies 2022, 15, 751.
https://doi.org/10.3390/en15030751
Academic Editors: Luis Hernández
Callejo, Sergio Nesmachnow and
Sara Gallardo Saavedra
Received: 2 January 2022
Accepted: 15 January 2022
Published: 20 January 2022
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Article
Wind Speed Prediction for Offshore Sites Using a Clockwork
Recurrent Network
Yuxuan Shi * , Yanyu Wang and Haoran Zheng
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;
wangyanyu@shu.edu.cn (Y.W.); zhrzhr@shu.edu.cn (H.Z.)
* Correspondence: shiyuxuan@shu.edu.cn
Abstract:
Offshore sites show greater potential for wind energy utilization than most onshore sites.
When planning an offshore wind power farm, the speed of offshore wind is used to estimate various
operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate
speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike
onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will
cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties
to offshore wind speed predictions, which traditional recurrent neural networks cannot deal with
for lacking in long-term dependency. An offshore wind speed prediction method is proposed by
using a clockwork recurrent network (CWRNN). In a CWRNN model, the hidden layer is subdivided
into several parts and each part is allocated a different clock speed. Under the mechanism, the long-
term dependency of the recurrent neural network can be easily addressed, which can furthermore
effectively solve the problem of strong nonlinearity in offshore speed winds. The experiments are
performed by using the actual data of two different offshore sites located in the Caribbean Sea and
one onshore site located in the interior of the United States, to verify the performance of the model.
The results show that the prediction model achieves significant accuracy improvement.
Keywords: clockwork recurrent network; offshore site; strong nonlinearity; wind speed prediction
1. Introduction
With the increasingly severe global climate problem, the sustainability of traditional
fossil fuels is facing huge challenges, and the development of renewable energy (RE) is
becoming inevitable [
1
]. RE, including wind energy, geothermal energy, and solar energy,
cannot only reduce carbon emissions, but also achieve sustainable development [
2
,
3
]. As
one form of RE, wind energy is widely used around the world on account of its wide
distribution, huge reserves, and environmental friendliness [
4
]. At the same time, wind
power is also one of the most commercially viable and dynamic RE sources due to its low
cost and permanent nature. On account of its relatively mature technology and commercial
conditions for large-scale development, wind energy has been the fastest growing energy
source in recent years. [
5
]. According to the data from the Global Wind Energy Council,
global wind power is accelerating its deployment, driven by the carbon-neutral trend. The
latest data show that the total global wind power bidding volume in the first quarter of
2021 is 6970 MW, 1.6 times that of the same period last year [6].
However, wind energy resources are susceptible to environmental changes, such as
geography, climate, and seasons. It brings great difficulties to wind power utilization. In
addition, the ecological problem with wind power is that it may disturb birds. Therefore,
accurate offshore wind speed prediction is of great help to the development of wind power.
However, there are still some factors that affect the prediction accuracy, among which
the major challenge is historical data. Regrettably, potential offshore sites have not had
enough records of wind speed for various reasons in the past. Consequently, it is a major
Energies 2022, 15, 751. https://doi.org/10.3390/en15030751 https://www.mdpi.com/journal/energies
