论文标题
短期风速预测的进化深度学习方法：Lillgrund海上风电场的案例研究
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm
论文作者
论文摘要
准确的短期风速预测对于风能的大规模整合至关重要。但是，风速的季节性和随机特征使预测一项艰巨的任务。这项研究使用了一种新的混合进化方法，该方法使用流行的进化搜索算法CMA-ES来调整两种长期短期记忆（LSTM）ANN模型的超参数以进行风预测。拟议的混合动力方法对从位于波罗的海的瑞典风电场的海上风力涡轮机收集的数据进行了培训。在我们的实验中考虑了两个预测范围，包括前十分钟（绝对短期）和提前一小时（短期）。我们的实验结果表明，新方法优于其他五个应用机器学习模型，即多项式神经网络（PNN），馈送前向神经网络（FNN），非线性自动回忆神经网络（NAR）和自适应神经性神经封面推理系统（ANFIS），如五个性能评价。
Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two Long short-term memory(LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea. Two forecasting horizons including ten-minutes ahead (absolute short term) and one-hour ahead (short term) are considered in our experiments. Our experimental results indicate that the new approach is superior to five other applied machine learning models, i.e., polynomial neural network (PNN), feed-forward neural network (FNN), nonlinear autoregressive neural network (NAR) and adaptive neuro-fuzzy inference system (ANFIS), as measured by five performance criteria.
