Hybrid Transformer Network for Different Horizons-based Enriched Wind
Speed Forecasting
- URL: http://arxiv.org/abs/2204.09019v1
- Date: Thu, 7 Apr 2022 12:03:53 GMT
- Title: Hybrid Transformer Network for Different Horizons-based Enriched Wind
Speed Forecasting
- Authors: Dr. M. Madhiarasan and Prof. Partha Pratim Roy
- Abstract summary: 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.
Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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