Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization

Convolutional neural networks a type of deep learning technology, are used for forecasting future power usage. The mean absolute error, mean square error, root mean square error, and mean constant percentage error are used to evaluate the performance of the models. These metrics are used to rank the...

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Bibliographic Details
Published in:	Electric power components and systems Vol. 51; no. 19; pp. 2397 - 2413
Main Authors:	Shekhar, Himanshu, Bhushan Mahato, Chandra, Suman, Sanjay Kumar, Singh, Satyanand, Bhagyalakshmi, L., Prasad Sharma, Mahendra, Laxmi Kantha, B., T, Helan Vidhya, Agraharam, Siva Kumar, Rajaram, A.
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 26-11-2023 Taylor & Francis Ltd
Subjects:	Artificial neural networks Deep learning demand side power control Energy consumption Energy sources Machine learning Mean square errors Mean square values Neural networks Optimization Power consumption Recurrent neural networks Renewable energy renewable energy systems Renewable resources Root-mean-square errors Solar energy stacked CNN Wind turbines
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Summary:	Convolutional neural networks a type of deep learning technology, are used for forecasting future power usage. The mean absolute error, mean square error, root mean square error, and mean constant percentage error are used to evaluate the performance of the models. These metrics are used to rank the models. Among the models tested, the CNN stack demonstrates the highest precision in estimating energy consumption and solar power output, with a mean absolute error of 0.015% points, a root mean square error of 0.23% points, and an average absolute percentage deviation of 1.71% points. However, for wind turbine (WT) energy generation, the recurrent neural network proves for most accurate model, achieving 0.070 as the median absolute percentage error is 2.65, Both the root of the mean square error and the average absolute error are 0.38. The training and validation data utilized in this study comprise the International Renewable Energy Agency's data on solar power production, WT power generation, and the "ensue" dataset, which includes hourly power consumption information from the Pennsylvania, New Jersey, and Maryland interconnection.
ISSN:	1532-5008 1532-5016
DOI:	10.1080/15325008.2023.2246463