A kernel extreme learning machine-based neural network to forecast very short-term power output of an on-grid photovoltaic power plant

2020 ◽  
Vol 43 (4) ◽  
pp. 395-412 ◽  
Author(s):  
Ceyhun Yildiz ◽  
Hakan Acikgoz
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Short Term ◽  
Photovoltaic Power ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

scholarly journals Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings

2019 ◽  
Vol 12 (6) ◽  
pp. 1077-1093 ◽  
Author(s):  
Primož Potočnik ◽  
Boris Vidrih ◽  
Andrej Kitanovski ◽  
Edvard Govekar
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Short Term ◽  
Term Prediction ◽  
Learning Machine ◽  
Short Term Prediction

scholarly journals Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3247 ◽  
Author(s):  
Dongkyu Lee ◽  
Jinhwa Jeong ◽  
Sung Hoon Yoon ◽  
Young Tae Chae
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Recurrent Neural Network ◽  
Power Output ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Feature Engineering ◽  
Short Term ◽  
Interaction Detection ◽  
Photovoltaic Power

The time resolution and prediction accuracy of the power generated by building-integrated photovoltaics are important for managing electricity demand and formulating a strategy to trade power with the grid. This study presents a novel approach to improve short-term hourly photovoltaic power output predictions using feature engineering and machine learning. Feature selection measured the importance score of input features by using a model-based variable importance. It verified that the normative sky index in the weather forecasted data had the least importance as a predictor for hourly prediction of photovoltaic power output. Six different machine-learning algorithms were assessed to select an appropriate model for the hourly power output prediction with onsite weather forecast data. The recurrent neural network outperformed five other models, including artificial neural networks, support vector machines, classification and regression trees, chi-square automatic interaction detection, and random forests, in terms of its ability to predict photovoltaic power output at an hourly and daily resolution for 64 tested days. Feature engineering was then used to apply dropout observation to the normative sky index from the training and prediction process, which improved the hourly prediction performance. In particular, the prediction accuracy for overcast days improved by 20% compared to the original weather dataset used without dropout observation. The results show that feature engineering effectively improves the short-term predictions of photovoltaic power output in buildings with a simple weather forecasting service.

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