Wind speed forecasting using a new multi-factor fusion and multi-resolution ensemble model with real-time decomposition and adaptive error correction

2020 ◽  
Vol 217 ◽  
pp. 112995 ◽  
Author(s):  
Hui Liu ◽  
Rui Yang ◽  
Zhu Duan
Keyword(s):  
Wind Speed ◽  
Error Correction ◽  
Real Time ◽  
Ensemble Model ◽  
Wind Speed Forecasting ◽  
Time Decomposition

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach

Energy ◽  
2022 ◽  
Vol 239 ◽  
pp. 122128
Author(s):  
Rui Yang ◽  
Hui Liu ◽  
Nikolaos Nikitas ◽  
Zhu Duan ◽  
Yanfei Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Wind Speed ◽  
Error Correction ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Multiple Error

A hybrid method for short-term wind speed forecasting based on Bayesian optimization and error correction

2021 ◽  
Vol 13 (3) ◽  
pp. 036101
Author(s):  
Xiuting Guo ◽  
Changsheng Zhu ◽  
Jie Hao ◽  
Shengcai Zhang ◽  
Lina Zhu
Keyword(s):  
Wind Speed ◽  
Error Correction ◽  
Hybrid Method ◽  
Bayesian Optimization ◽  
Short Term ◽  
Wind Speed Forecasting

A novel deep learning ensemble model with data denoising for short-term wind speed forecasting

2020 ◽  
Vol 207 ◽  
pp. 112524 ◽  
Author(s):  
Zhiyun Peng ◽  
Sui Peng ◽  
Lidan Fu ◽  
Binchun Lu ◽  
Junjie Tang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Ensemble Model ◽  
Short Term ◽  
Wind Speed Forecasting
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