| Summary: | 碩士 === 逢甲大學 === 產業研發碩士班 === 107 === In response to the issues of climate change and the pursuit of sustainable development in the world. In renewable energy, wind power is an indispensable part. Taiwan has a good geographical area in the west, as well as the promotion of conditions and policies, and the cost reduction year by year. They are all making Taiwan more suitable for developing wind power, as well as conducting related research and evaluating its benefits.
This paper proposes to combine the two methods of Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM) based on deep learning. These techniques take advantages of the CNN's expertise in feature extraction and LSTM are good at processing time series data. Then, the results are subjected to point forecast and probabilistic forecast evaluation. In them also uses multi-step forecast to output and observe the forecast results at different time points. The probabilistic forecast uses Empirical Cumulative Distribution Function (ECDF) to define the upper and lower limits of the prediction interval.
In order to prove the accuracy of the proposed method for wind power generation, the CNN model and LSTM models are also predicted to verify whether the proposed model is complete and the effective features in the data are extracted to improve learning accuracy and predict. In this way, the three-party comparison is carried out with point forecast, probabilistic forecast, multi-step forecast, multi-step probabilistic forecast and probabilistic forecast results of different prediction intervals to verify that the proposed model performance and its stability are better than the other two methods.
Index Terms- Deep learning, machine learning, point forecast, probabilistic forecast, multi-step forecast, CNN, LSTM, feature extraction, ECDF
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