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Keywords: Neural Networks
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Journal Articles
Article Type: Research Papers
J. Sol. Energy Eng. October 2021, 143(5): 051003.
Paper No: SOL-20-1179
Published Online: February 23, 2021
... individual predictors are arranged to predict solar radiation intensity using historical weather and solar radiation records. Three stacking techniques, namely, feed-forward neural networks, support vector regressors, and k-nearest neighbor regressors, have been examined and compared to combine...
Journal Articles
Article Type: Research-Article
J. Sol. Energy Eng. June 2015, 137(3): 031011.
Paper No: SOL-14-1104
Published Online: June 1, 2015
... is a parameter almost yearly and site independent. To develop the ST models and MOS technique, the ANN multilayer perceptron neural network (MLPNN) algorithm was used. 1 Corresponding author. Contributed by the Solar Energy Division of ASME for publication in the J OURNAL OF S OLAR E NERGY E...
Journal Articles
Article Type: Research-Article
J. Sol. Energy Eng. August 2013, 135(3): 031007.
Paper No: SOL-12-1023
Published Online: March 26, 2013
... developed on multiple sensor data (scenario 4) was used for jerk prediction. A neural network using the BFGS learning method outperformed algorithms such as CG, GD, and RBF. The Broyden–Fletcher–Goldfarb–Shanno neural network models accurately predicted jerk in a ring gear at different time intervals...
Journal Articles
Article Type: Research Papers
J. Sol. Energy Eng. August 2010, 132(3): 031008.
Published Online: June 14, 2010
...-mining algorithms are used to build models with turbine parameters of interest as inputs, and the vibrations of drive train and tower as outputs. The performance of each model is thoroughly evaluated based on metrics widely used in the wind industry. The neural network algorithm outperforms other...
Journal Articles
Article Type: Technical Papers
J. Sol. Energy Eng. August 2003, 125(3): 331–342.
Published Online: August 4, 2003
...Moncef Krarti An overview of commonly used methodologies based on the artificial intelligence approach is provided with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. A description of selected applications to building energy systems of AI approaches is outlined...