Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Kumar Shivam,Shang-Chen Wu,Jong-Chyuan Tzou

doi:10.3390/en13071772

Abstract

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

Highlights

With increasing concern about global warming and pollution caused by over usage of fossil fuels, the leading world organizations and countries are encouraging renewable energy sources like wind and solar power
We investigated a residual U-net architecture (ResUnet) architecture, consisting of the residual blocks from researchers have proposed a hybrid model by replacing the plain convolution layers of U-net with
By accurately predicting distant future timesteps, efficient power management and planning of repair/cleaning schedules could be done in advance

Summary

Introduction

With increasing concern about global warming and pollution caused by over usage of fossil fuels, the leading world organizations and countries are encouraging renewable energy sources like wind and solar power. According to the U.S Energy Information Administration, renewable energy contributes to over 18% of total energy production with solar and wind energy contributing almost 10% [1]. Wind energy is mostly generated at on-shore or off-shore clusters of wind turbines known as wind farms. These wind farms in order to manage the future production of total electrical power efficiently require prior knowledge of future wind conditions. According to [2], there are four temporal ranges for forecasting: (1) long-term, with a range of a week to a year or much ahead; (2) medium-term, for two days to a week ahead;

Methods

Results

Conclusion