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- W4226082183 abstract "This letter proposes a transferrable model-agnostic meta-learning (T-MAML) approach for short-term load forecasting for single households. The proposed approach enables multiple households to collaboratively train a generic artificial neural network (ANN) model. The generic ANN model is then further trained at each target household node for the STLF purpose. The proposed T-MAML based STLF approach is featured by: (1) significant reduction of computation and communication costs on the household side; and (2) superior STLF performance, especially when there is limited load data for training in a target household. Experiments based on a real Australian residential dataset are conducted to validate the effectiveness of the proposed approach." @default.
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- W4226082183 date "2022-07-01" @default.
- W4226082183 modified "2023-09-26" @default.
- W4226082183 title "Transferrable Model-Agnostic Meta-learning for Short-Term Household Load Forecasting With Limited Training Data" @default.
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- W4226082183 doi "https://doi.org/10.1109/tpwrs.2022.3169389" @default.
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