Abstract
Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting models. This paper introduces a new approach using variational auto-encoding and hybrid transfer learning to forecast wind power for large-scale regional windfarms. Transfer learning is applied to windfarm data collections to boost model training. However, multiregional windfarms consist of different wind and weather conditions, which makes it difficult to apply transfer learning. Therefore, we propose a hybrid transfer learning method consisting of two feature spaces; the first was obtained from an already trained model, while the second, small feature set was obtained from a current windfarm for retraining. Finally, the hybrid transferred neural networks were fine-tuned for different windfarms to achieve precise power forecasting. A comparison with other state-of-the-art approaches revealed that the proposed method outperforms previous techniques, achieving a lower mean absolute error (MAE), i.e., between 0.010 to 0.044, and a lowest root mean square error (RMSE), i.e., between 0.085 to 0.159. The normalized MAE and RMSE was 0.020, and the accuracy losses were less than 5%. The overall performance showed that the proposed hybrid model offers maximum wind power forecasting accuracy with minimal error.
Highlights
Renewable energy is one of the fastest-growing energy sectors, accounting for 29% of global power output in 2020; global energy consumption will increase by 56% between 2010 to 2040 [1]
Wind energy plays a prominent role in the renewable energy sector for the generation of electricity, and wind power forecasting is a useful strategy for improving wind turbine accuracy [6]
The forecasting assessment revealed that the proposed forecasting model is very effective for forecasting wind energy, while transfer learning is more suitable for improving forecasting outcomes with less training on large and complex regional windfarms
Summary
Renewable energy is one of the fastest-growing energy sectors, accounting for 29% of global power output in 2020; global energy consumption will increase by 56% between 2010 to 2040 [1]. The increase in electricity generation has been attributed to significant economic progress in recent years, which has contributed to the negative impact on the climate, especially in developing countries [2]. The United Kingdom’s overall renewable energy production was 119 TWh in 2019, up by 8.5% from the previous year; a huge share of this growth was associated with wind energy, which comprised about 64 TWh (53.7%) With this increased efficiency, the United Kingdom has surpassed China, United States, Germany, India, and Spain to become the world’s sixth-largest renewable energy generator. - An empirical evaluation of previous forecasting approaches demonstrated that the proposed transfer learning-based wind power forecasting technique is quite effective, producing better accuracy with minimal retraining.
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