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.

本文提出了一种新颖的智能混合风速预测模型（ICEEMDAN-TNF-MLPN-RECS），相较于现有技术在不同的时间范围内实现了更高的精确度，降低了电力系统工程师的工作量。

基于不同时域的增强风速混合变压器网络预测

Hybrid Transformer Network for Different Horizons-based Enriched Wind  Speed Forecasting

The paper presents a spatio-temporal wind speed forecasting algorithm using
Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated
by recent advances in renewable energy integration and smart grids, we apply
our proposed algorithm for wind speed forecasting. Renewable energy resources
(wind and solar)are random in nature and, thus, their integration is
facilitated with accurate short-term forecasts. In our proposed framework, we
model the spatiotemporal information by a graph whose nodes are data generating
entities and its edges basically model how these nodes are interacting with
each other. One of the main contributions of our work is the fact that we
obtain forecasts of all nodes of the graph at the same time based on one
framework. Results of a case study on recorded time series data from a
collection of wind mills in the north-east of the U.S. show that the proposed
DL-based forecasting algorithm significantly improves the short-term forecasts
compared to a set of widely-used benchmarks models.

本文利用深度学习与循环神经网络算法，提出一种时空风速预测模型，并利用其进行风能预测。研究结果表明，该模型不仅可以显著提高短期预测水平，同时可以模拟并预测获得更多数据的实体之间的相互作用。