Journal
Publisher
IEEE
DOI: 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00145
Keywords
Short-term load forecasting; Deep learning; Ensemble learning; Long short-term memory (LSTM); Levenberg-Marquardt (LM) algorithm
Categories
Funding
- NSF [DMS-1736470]
- Wireless Engineering Research and Education Center (WEREC) at Auburn University
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In this paper, a short-term load forecasting framework with long short-term memory (LSTM)-based ensemble learning is proposed. To fully exploit the correlation in data for accurate load forecasting, the data is first clustered and each cluster is used to train an LSTM model. Then a Fully Connected Cascade (FCC) Neural Network is incorporated for ensemble learning, which is solved by an enhanced Levenberg-Marquardt (LM) training algorithm. The proposed framework is tested with a public dataset, where its superior performance over several baseline schemes is demonstrated.
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