Open AccessForecasting renewable energy for environmental resilience through computational intelligence
Author(s) -
Mansoor Khan,
Essam A. AlAmmar,
Muhammad Rashid Naeem,
Wonsuk Ko,
Hyeong-Jin Choi,
Hyun-Koo Kang
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0256381
Subject(s) - wind power , mean squared error , wind power forecasting , offshore wind power , computer science , renewable energy , resilience (materials science) , deep learning , artificial intelligence , key (lock) , power (physics) , machine learning , electric power system , statistics , engineering , mathematics , physics , quantum mechanics , electrical engineering , computer security , thermodynamics
Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.