3 Deep Neural Networks for Energy Load Forecasting
亮點:現在以爲負荷數據上做一維卷積，在和一起時間天氣因素一起輸入到全連接網絡做預測 用於能量負荷預測的深度神經網絡 本文介紹的工作研究使用卷積神經網絡的有效性 用於執行能量負荷預測的網絡（CNN）個人建築水平。所提出的方法使用關於歷史載荷的卷積。輸出來自 卷積操作被饋送到完全連接的層與其他相關信息。提出的方法是 在電力消耗的基準數據集上實施對於單個住宅客戶。 從CNN獲得的結果與長期短期獲
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相關文章
- 1. 9、Building Energy Load Forecasting using Deep Neural Networks
- 2. Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
- 3. 8、Energy Load Forecast Using S2S Deep Neural Networks with k-Shape Clustering
- 4. Improving Deep Neural Networks[3]
- 5. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 6. Aggregated Residual Transformations for Deep Neural Networks
- 7. Deep Neural Networks for Object Detection
- 8. neural networks and deep learning-chapter1-3
- 9. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 10. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning