论文标题
混合变压器网络，用于基于不同视野的丰富风速预测
Hybrid Transformer Network for Different Horizons-based Enriched Wind Speed Forecasting
论文作者
论文摘要
高度准确的基于视野的风速预测有助于更好的现代电力系统。本文提出了一种新颖的精明杂交风速预测模型，并将其应用于不同的视野。提出的混合预测模型使用自适应噪声（ICEEMDAN）改进了完整的合奏经验模式分解，将原始风速数据分解为IMF（内在模式函数）。我们将获得的子系列从Iceemdan提供给了变压器网络。每个变压器网络都计算预测子系列，然后传递到融合阶段。从单个变压器网络预测子层的融合中获取主要风速预测。使用多层感知神经网络估计残差误差值并预测误差。预测误差将添加到主要预测风速中，以利用风速预测的高精度。与实时Kethanur的比较分析印度风电场数据集结果揭示了拟议的Iceemdan-TNF-MLPN-RECS Hybrid Model与MAE的出色性能= 1.7096*10^-07，Mape = 2.8416*10^-06，MRE = 2.8416*10^-08，MSE = 5.08，MSE = 5.00206*10^-14，以及RMSE = 2.2407*10^-07用于案例研究1和MAE = 6.1565*10^-07，Mape = 9.5005*10^-06，MRE = 9.5005*10^-08，MSE = 8.9289*8.9289*10^-13，and Rmse = 9.4493*10^-0^10^-07并减轻电力系统工程师的负担。
Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries from ICEEMDAN to the transformer network. Each transformer network computes the forecast subseries and then passes to the fusion phase. Get the primary wind speed forecasting from the fusion of individual transformer network forecast subseries. Estimate the residual error values and predict errors using a multilayer perceptron neural network. The forecast error is added to the primary forecast wind speed to leverage the high accuracy of wind speed forecasting. Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06, MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1 and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08, MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.
