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Performance evaluation of short-term cross-building energy predictions using deep transfer learning strategies

Energy and Buildings(2022)

Cited 10|Views16
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Abstract
•3 DTL strategies obtain 75% PIRs than LSTM for data shortage BEP tasks in OFs and UCs.•Recommend Fine-tune for next few weeks BEP by balance between time cost and performance.•Recommend DANN for nearly a year BEP by outperformed accuracy than Fine-tune, DaNN.•Evaluate DTL prediction accuracy by varying available training data in both source and target buildings.
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Key words
Building energy prediction (BEP),Cross-building,Deep transfer learning (DTL),Domain adversarial neural network (DANN),Fine-tune,Performance improvement ratio (PIR)
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