scholarly journals Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature

2020 ◽  
Vol 6 ◽  
pp. 273-286 ◽  
Author(s):  
Sambeet Mishra ◽  
Chiara Bordin ◽  
Kota Taharaguchi ◽  
Ivo Palu
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Deep Learning ◽  
Wind Power ◽  
Wind Power Generation ◽  
Learning Models ◽  
Multivariate Prediction

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

Influence of Deep Learning on Precision Improvement in Predictive Models of Wind Power Generation

2017 ◽  
Author(s):  
Ronaldo R.B. de Aquino ◽  
Jonata C. Albuquerque ◽  
Otoni Nobrega Neto ◽  
Milde M.S. Lira ◽  
Aida A. Ferreira ◽  
...  
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Predictive Models ◽  
Wind Power Generation

scholarly journals A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4964 ◽  
Author(s):  
Bingchun Liu ◽  
Shijie Zhao ◽  
Xiaogang Yu ◽  
Lei Zhang ◽  
Qingshan Wang
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Wind Power ◽  
Short Term Memory ◽  
Wind Power Generation ◽  
Power Grids ◽  
Stable Operation ◽  
Original Time Series ◽  
Input Variables ◽  
Original Time

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

Predicting Wind Power Generation using Hybrid Deep Learning with Optimization

2021 ◽  
pp. 1-1
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Evan Mac A Gray ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Wind Power Generation

scholarly journals Less Information, Similar Performance: Comparing Machine Learning-Based Time Series of Wind Power Generation to Renewables.ninja

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2277
Author(s):  
Johann Baumgartner ◽  
Katharina Gruber ◽  
Sofia G. Simoes ◽  
Yves-Marie Saint-Drenan ◽  
Johannes Schmidt
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Time Series ◽  
Wind Power ◽  
Wind Power Generation ◽  
Location Information ◽  
Similar Performance ◽  
Input Dataset ◽  
Synthetic Time Series

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve-based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation, despite their need for accurate location information and bias correction, as well as their insufficient replication of extreme events and short-term power ramps. In this paper, we assessed how time series generated by machine learning models (MLMs) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we applied neural networks to one wind speed input dataset derived from MERRA2 reanalysis with no location information and two with additional location information. The resulting time series and RN time series were compared with actual generation. All MLM time series feature an equal or even better time series quality than RN, depending on the characteristics considered. We conclude that MLM models show a similar performance to RN, even when information on turbine locations and turbine types is unavailable.

scholarly journals Multi-decadal offshore wind power variability can be mitigated through optimized European allocation

2020 ◽  
Author(s):  
Charlotte Neubacher ◽  
Jan Wohland ◽  
Dirk Witthaut
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Wind Power ◽  
North Sea ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
The North ◽  
The North Sea

<p>Wind power generation is a promising technology to reduce greenhouse gas emissions in line with the Paris Agreement.  In the recent years, the global offshore wind market grew around 30% per year but the full potential of this technology is still not fully exploited. In fact, offshore wind power has the potential to generate more than the worldwide energy demand of today. The high variability of wind on many different timescales does, however, pose serious technical challenges for system integration and system security.  With a few exceptions, little focus has been given to multi-decadal variability. Our research therefore focuses on timescales exceeding ten years.</p><p>Based on detrended wind data from the coupled centennial reanalysis CERA-20C, we calculate long-term offshore wind power generation time series across Europe and analyze their variability with a focus on the North Sea, the Mediterranean Sea and the Atlantic Ocean. Our approach is based on two independent spectral analysis methods, namely power spectral density and singular spectrum analysis. The latter is particularly well suited for relatively short and noisy time series. In both methods an AR(1)-process is considered as a realistic model for the noisy background. The analysis is complemented by computing the 20yr running mean to also gain insight into long term developments and quantify benefits of large-scale balancing.</p><p>We find strong indications for two significant multidecadal modes, which appear consistently independent of the statistical method and at all locations subject to our investigation. Moreover, we reveal potential to mitigate multidecadal offshore wind power generation variability via spatial balancing in Europe. In particular, optimized allocations off the Portuguese coast and in the North Sea allow for considerably more stable wind power generation on multi-decadal time scales.</p>

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