Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM

Liu, Xiaolei, Lin, Zi and Feng, Ziming (2021) Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM. Energy, 227. p. 120492. ISSN 0360-5442

[img] Text
Wind.pdf - Accepted Version
Restricted to Repository staff only until 30 March 2022.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (1MB) | Request a copy
Official URL: https://doi.org/10.1016/j.energy.2021.120492

Abstract

Offshore wind power is one of the fastest-growing energy sources worldwide, which is environmentally friendly and economically competitive. Short-term time series wind speed forecasts are extremely significant for proper and efficient offshore wind energy evaluation and in turn, benefit wind farm owner, grid operators as well as end customers. In this study, a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model is proposed to predict hourly-measured wind speeds in the coastal/offshore area of Scotland. The used datasets consist of three wind speed time series collected at different elevations from a coastal met mast, which was designed to serve for a demonstration offshore wind turbine. To verify SARIMA’s performance, the developed predictive model was further compared with the newly developed deep-learning-based algorithms of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). Regardless of the recent development of computational power has triggered more advanced machine learning algorithms, the proposed SARIMA model has shown its outperformance in the accuracy of forecasting future lags of offshore wind speeds along with time series. The SARIMA model provided the highest accuracy and robust healthiness among all the three tested predictive models based on corresponding datasets and assessed forecasting horizons.

Item Type: Article
Uncontrolled Keywords: Wind speed forecasting, Seasonal auto-regression integrated moving average (SARIMA), Deep learning, Long short term memory (LSTM), Gated recurrent unit (GRU)
Subjects: F800 Physical and Terrestrial Geographical and Environmental Sciences
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 30 Apr 2021 10:21
Last Modified: 31 Jul 2021 16:45
URI: http://nrl.northumbria.ac.uk/id/eprint/46064

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics