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Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energyopen access

Authors
Galindo, Padilha G.A.Ko, J.Jung, Jason J.de, Mattos Neto P.S.G.
Issue Date
Nov-2022
Publisher
MDPI
Keywords
hybrid systems; Machine Learning; renewable energy; time series; transformers
Citation
Applied Sciences (Switzerland), v.12, no.21
Journal Title
Applied Sciences (Switzerland)
Volume
12
Number
21
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59522
DOI
10.3390/app122110985
ISSN
2076-3417
2076-3417
Abstract
In recent years, the use of renewable energy has grown significantly in electricity generation. However, the output of such facilities can be uncertain, affecting their reliability. The forecast of renewable energy production is necessary to guarantee the system’s stability. Several authors have already developed deep learning techniques and hybrid systems to make predictions as accurate as possible. However, the accurate forecasting of renewable energy still is a challenging task. This work proposes a new hybrid system for renewable energy forecasting that combines the traditional linear model (Seasonal Autoregressive Integrated Moving Average—SARIMA) with a state-of-the-art Machine Learning (ML) model, Transformer neural network, using exogenous data. The proposal, named H-Transformer, is compared with other hybrid systems and single ML models, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN), using five data sets of wind speed and solar energy. The proposed H-Transformer attained the best result compared to all single models in all datasets and evaluation metrics. Finally, the hybrid H-Transformer obtained the best result in most cases when compared to other hybrid approaches, showing that the proposal can be a useful tool in renewable energy forecasting. © 2022 by the authors.
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