Abstract
Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents' activities, individual residential loads are usually too volatile to forecast accurately. An LSTM based deep learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.
Original language | English |
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Pages (from-to) | 1087-1088 |
Number of pages | 2 |
Journal | IEEE Transactions on Power Systems |
Volume | 33 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2018 |
Externally published | Yes |
Keywords
- Deep learning
- Meter-level load forecasting
- Recurrent neural network
- Short-Term load forecasting