Abstract
Studies have demonstrated that changes in the climate affect wind power forecasting under different weather conditions. Theoretically, accurate prediction of both wind power output and weather changes using statistics-based prediction models is difficult. In practice, traditional machine learning models can perform long-term wind power forecasting with a mean absolute percentage error (MAPE) of 10% to 17%, which does not meet the engineering requirements for our renewable energy project. Deep learning networks (DLNs) have been employed to obtain the correlations between meteorological features and power generation using a multilayer neural convolutional architecture with gradient descent algorithms to minimize estimation errors. This has wide applicability to the field of wind power forecasting. Therefore, this study aimed at the long-term (24–72-h ahead) prediction of wind power with an MAPE of less than 10% by using the Temporal Convolutional Network (TCN) algorithm of DLNs. In our experiment, we performed TCN model pretraining using historical weather data and the power generation outputs of a wind turbine from a Scada wind power plant in Turkey. The experimental results indicated an MAPE of 5.13% for 72-h wind power prediction, which is adequate within the constraints of our project. Finally, we compared the performance of four DLN-based prediction models for power forecasting, namely, the TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models. We validated that the TCN outperforms the other three models for wind power prediction in terms of data input volume, stability of error reduction, and forecast accuracy.
Highlights
With the increasingly serious global warming crisis and the burning of fossil fuels inducing air pollution and climate change, concerned parties have begun to invest in the development and application of renewable energy
To investigate the forecast accuracy of day-ahead for wind turbines measured with a performance evaluation index (i.e., mean absolute percentage error (MAPE)), we developed a featurebased learning model for wind power forecasting and trained Temporal Convolutional Network (TCN) [20,21,22,23] to learn meteorological features and identify the output class of power generation
The performance of the proposed TCN-based model for wind power prediction is demonstrated by means of an example
Summary
With the increasingly serious global warming crisis and the burning of fossil fuels inducing air pollution and climate change, concerned parties have begun to invest in the development and application of renewable energy. Wind power forecasting prediction models can be classified using the following three approaches: (1) the physical approach, in which weather changes are considered as deterministic events [1], (2) the statistical approach, in which weather changes are considered as a random process [2,3], and (3) the hybrid approach, which constitutes a weighted aggregation of the other two prediction models [4,5,6,7,8,9] Compared with these three methods for wind power prediction problems, deep learning network (DLN) approaches, such as Boltzmann machines (RBM), long short-term memory (LSTM), temporal convolutional networks (TCN), and convolutional neural networks (CNN) have exhibited superior results and are generally considered as an alternative solution for wind power prediction [10,11]
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