An adaptive backpropagation algorithm for long-term electricity load forecasting

Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for long-term forecasting. This inaccuracy is attributed to insufficient...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 34; no. 1; pp. 477 - 491
Main Authors:	Mohammed, Nooriya A., Al-Bazi, Ammar
Format:	Journal Article
Language:	English
Published:	London Springer London 01.01.2022 Springer Nature B.V
Subjects:	Adaptive algorithms Algorithms Artificial Intelligence Artificial neural networks Back propagation Back propagation networks Best practice Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Economic forecasting Electric power demand Electrical loads Electricity Electricity consumption Energy consumption Forecasting Formulations Image Processing and Computer Vision Learning theory Machine learning Mathematical models Multilayers Neural networks Original Original Article Probability and Statistics in Computer Science Recurrent neural networks Long-term forecasting Recurrent neural networks Linear regression MLP neural networks Radial basis function networks Load demand Adaptive backpropagation
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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