Combination of Manifold Learning and Deep Learning Algorithms for Mid-Term Electrical Load Forecasting
| Autor: | Wei Dai, Jinghua Li, Shanyang Wei |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Artificial neural network
Electrical load Computer Networks and Communications business.industry Computer science Dimensionality reduction Deep learning Nonlinear dimensionality reduction Machine learning computer.software_genre Computer Science Applications Electric power system Nonlinear system Artificial Intelligence Artificial intelligence business computer Software Curse of dimensionality |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 34:2584-2593 |
| ISSN: | 2162-2388 2162-237X |
| Popis: | Mid-term load forecasting (MTLF) is of great significance for power system planning, operation, and power trading. However, the mid-term electrical load is affected by the coupling of multiple factors and demonstrates complex characteristics, which leads to low prediction accuracy in MTLF. Furthermore, MTLF is faced with the ``curse of dimensionality'' problem due to a large number of variables. This article proposes an MTLF method based on manifold learning, which can extract the underlying factors of load variations to help improve the accuracy of MTLF and significantly reduce the calculation. Unlike linear dimensionality reduction methods, manifold learning has better nonlinear feature extraction ability and is more suitable for load data with nonlinear characteristics. Furthermore, long short-term memory (LSTM) neural networks are used to establish forecasting models in the low-dimensional space obtained by manifold learning. The proposed MTLF method is tested on independent system operator (ISO) New England datasets, and load forecasting in 24, 168, and 720 h ahead is carried out. The numerical results validate that the proposed method has higher prediction accuracy than many mature methods in the mid-term time scale. |
| Databáze: | OpenAIRE |
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