Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm

2021 ◽  
Author(s):  
Yingchao Dong ◽  
Hongli Zhang ◽  
Cong Wang ◽  
Xiaojun Zhou
2021 ◽  
Vol 13 (3) ◽  
pp. 1164
Author(s):  
Guangyu Qin ◽  
Qingyou Yan ◽  
Jingyao Zhu ◽  
Chuanbo Xu ◽  
Daniel M. Kammen

Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity spot trading market. However, the instability of the wind power series makes the forecast difficult. To improve forecast accuracy, a hybrid optimization algorithm is established in this study, which combines variational mode decomposition (VMD), maximum relevance & minimum redundancy algorithm (mRMR), long short-term memory neural network (LSTM), and firefly algorithm (FA) together. Firstly, the original historical wind power sequence is decomposed into several characteristic model functions with VMD. Then, mRMR is applied to obtain the best feature set by analyzing the correlation between each component. Finally, the FA is used to optimize the various parameters LSTM. Adding the forecasting results of all sub-sequences acquires the forecasting result. It turns out that the proposed hybrid algorithm is superior to the other six comparison algorithms. At the same time, an additional case is provided to further verify the adaptability and stability of the proposed hybrid model.


2013 ◽  
Vol 133 (4) ◽  
pp. 366-372 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto

2019 ◽  
Vol 139 (3) ◽  
pp. 212-224
Author(s):  
Xiaowei Dui ◽  
Masakazu Ito ◽  
Yu Fujimoto ◽  
Yasuhiro Hayashi ◽  
Guiping Zhu ◽  
...  

Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.


Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.


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