Interpretable LSTM Based on Mixture Attention Mechanism for Multi-Step Residential Load Forecasting
Interpretable LSTM Based on Mixture Attention Mechanism for Multi-Step Residential Load Forecasting
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Scope and Contents
Contents
Residential load forecasting is of great significance to improve the energy efficiency of smart home services. Deep-learning techniques, i.e., long short-term memory (LSTM) neural networks, can considerably improve the performance of prediction models. However, these black-box networks are generally unexplainable, which creates an obstacle for the...
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Interpretable LSTM Based on Mixture Attention Mechanism for Multi-Step Residential Load Forecasting
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TN_cdi_proquest_journals_2693995086
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2693995086
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ISSN
2079-9292
E-ISSN
2079-9292
DOI
10.3390/electronics11142189
