研究表明，气候变化会影响不同天气条件下的风电功率预测。从理论上讲，使用基于统计的预测模型来准确预测风电输出和天气变化是很困难的。在实践中，传统的机器学习模型可以进行长期风电功率预测，平均绝对百分比误差（MAPE）在 10% 到 17% 之间，这不符合我们可再生能源项目的工程要求。深度学习网络 (DLN) 已被用于使用多层神经卷积架构和梯度下降算法来获得气象特征与发电之间的相关性，以最大限度地减少估计误差。这在风电功率预测领域具有广泛的适用性。所以，本研究旨在通过使用 DLN 的时间卷积网络 (TCN) 算法对 MAPE 小于 10% 的风功率进行长期（提前 24-72 小时）预测。在我们的实验中，我们使用历史天气数据和来自土耳其 Scada 风力发电厂的风力涡轮机的发电输出进行了 TCN 模型预训练。实验结果表明，72 小时风电功率预测的 MAPE 为 5.13%，这在我们项目的约束范围内是足够的。最后，我们比较了四种基于 DLN 的功率预测预测模型的性能，即 TCN、长短期记忆 (LSTM)、循环神经网络 (RNN) 和门控循环单元 (GRU) 模型。我们验证了 TCN 在数据输入量、减少误差的稳定性、
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.
