8、Energy Load Forecast Using S2S Deep Neural Networks with k-Shape Clustering
用S2S深度神經網絡預測能量負荷具有k形聚類的網絡 特別是長短期記憶（LSTM）具有序列到序列（S2S）架構的算法。 我們在預測聚合時，提高預測準確性通過使用k-Shape聚類算法加載網絡創建更容易預測的消費者羣體 本文中描述的方法在現實世界數據上進行了測試斯洛伐克企業的用電量。 電力消費者通常都有隨機行爲，使個人消費需求變得艱難預測。爲了解決這個問題的消費數據 是彙總的。出於這個原因，使用聚類分
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相關文章
- 1. 9、Building Energy Load Forecasting using Deep Neural Networks
- 2. 3 Deep Neural Networks for Energy Load Forecasting
- 3. SpectralNet : spectral clustering using deep neural networks
- 4. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
- 5. Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
- 6. ImageNet Classification with Deep Convolutional Neural Networks
- 7. Brain tumor segmentation with Deep Neural Networks
- 8. AlexNet：ImageNet Classification with Deep Convolutional Neural Networks
- 9. ImageNet Classification with Deep Convolutional Neural Networks
- 10. 【Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning】論文筆記