Estimation of Energy Produced in Hydroelectric Power Plant Industrial Automation Using Deep Learning and Hybrid Machine Learning Techniques

Today, due to the rapidly increasing environmental pollution, the importance of energy obtained from renewable energy sources is increasing. One of these important renewable energy sources is Hydroelectric Power Plants. Using hybrid-machine learning algorithms, non-hybrid machine learning algorithms...

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Veröffentlicht in:Electric power components and systems Jg. 49; H. 3; S. 213 - 232
1. Verfasser: Aksoy, Bekir
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 10.09.2021
Taylor & Francis Ltd
Schlagworte:
Accuracy
Alternative energy sources
Artificial intelligence
Artificial neural networks
Deep learning
Energy resources
Errors
Flow velocity
Genetic algorithms
hybrid machine learning algorithms
Hydroelectric plants
Hydroelectric power
hydroelectric power plant
Machine learning
Neural networks
Performance evaluation
Power plants
regression
Renewable energy sources
Renewable resources
ISSN:1532-5008, 1532-5016
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Zusammenfassung:Today, due to the rapidly increasing environmental pollution, the importance of energy obtained from renewable energy sources is increasing. One of these important renewable energy sources is Hydroelectric Power Plants. Using hybrid-machine learning algorithms, non-hybrid machine learning algorithms and deep learning models, the electrical energy produced by Hydroelectric Power Plant was estimated based on seventeen different input parameters such as flow rate and generator current. In this study, hyperparameters were tuned and feature selection was made with a Genetic Algorithm in order to increase the accuracy rate in artificial intelligence models. In addition, all artificial intelligence models were evaluated according to the processing time, Coefficient of determination, Mean Squared Error, Mean Absolute Error and Root Mean Square Error performance evaluation criteria. It was seen that the electrical energy production value was determined with the Random Forest Algorithm, which is one of the non-hybrid machine learning algorithms, with an accuracy rate of 99.641%, with Genetic Algorithm + Random Forest Algorithm, which is a hybrid machine learning algorithm, with an accuracy rate of 99.672% and with Deep Neural Network, which is one of the deep learning models, with a 99.99% accuracy rate. According to the results, it was determined that the Deep Neural Networks model determined the electrical energy production value with a higher accuracy rate compared to hybrid and non-hybrid machine learning algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1532-5008
1532-5016
DOI:10.1080/15325008.2021.1937401