Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network

Yuxuan Shi,Haoran Zheng,Yanyu Wang

doi:10.3390/en15030751

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.

Highlights

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]
The results show that back propagation neural network (BPNN) and convolutional neural network (CNN) have worse performances in wind speed prediction
The clockwork recurrent network (CWRNN) is another type of recurrent neural network (RNN), which breaks up the neurons in the hidden layer into different parts, and the neurons in the same part work at a given clock speed to address long term dependency

Summary

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, and 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]. Wind power is 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. 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]

Results

Discussion

Conclusion