Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models

Shahram Hanifi (), Saeid Lotfian (), Hossein Zare-Behtash and Andrea Cammarano
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Shahram Hanifi: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Saeid Lotfian: Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Hossein Zare-Behtash: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Andrea Cammarano: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

Energies, 2022, vol. 15, issue 19, 1-21

Abstract: The main obstacle against the penetration of wind power into the power grid is its high variability in terms of wind speed fluctuations. Accurate power forecasting, while making maintenance more efficient, leads to the profit maximisation of power traders, whether for a wind turbine or a wind farm. Machine learning (ML) models are recognised as an accurate and fast method of wind power prediction, but their accuracy depends on the selection of the correct hyperparameters. The incorrect choice of hyperparameters will make it impossible to extract the maximum performance of the ML models, which is attributed to the weakness of the forecasting models. This paper uses a novel optimisation algorithm to tune the long short-term memory (LSTM) model for short-term wind power forecasting. The proposed method improves the power prediction accuracy and accelerates the optimisation process. Historical power data of an offshore wind turbine in Scotland is utilised to validate the proposed method and compare its outcome with regular ML models tuned by grid search. The results revealed the significant effect of the optimisation algorithm on the forecasting models’ performance, with improvements of the RMSE of 7.89, 5.9, and 2.65 percent, compared to the persistence and conventional grid search-tuned Auto-Regressive Integrated Moving Average (ARIMA) and LSTM models.

Keywords: auto-regressive integrated moving average (ARIMA); long short-term memory (LSTM); Optuna; isolation forest (IF); elliptic envelope (EE); one-class support vector machine (OCSVM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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