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Conference Paper
RNN-LSTM 기반 공휴일 정보를 고려한 단기 전력수요예측
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- Issue Date
- 2016-11
- Citation
- 대한전자공학회 학술 대회 (추계) 2016, pp.552-555
- Publisher
- 대한전자공학회
- Language
- Korean
- Type
- Conference Paper
- Abstract
- Daily electricity demand and its fluctuation have increased by abrupt climate change and excessive use of air conditioning and these has affected to forecast the short-term electricity load. Also, the electricity load pattern learning is disturbed by holidays that cause sudden the electricity demand reduction. We proposed the feature extraction algorithm for demand reduction in holidays and implemented the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) based forecasting. The results were compared with the forecasting performance of SARIMA (Seasonal Auto Regressive Integrated Moving Average). The comparative result shows that RNN-LSTM outperforms SARIMA.
- KSP Keywords
- Abrupt climate change, Air conditioning, Auto Regressive Integrated Moving Average(ARIMA), Electricity Demand, Excessive use, Forecasting performance, Long-short term memory(LSTM), Pattern learning, Recurrent Neural Network(RNN), Short-term Electricity Load, demand reduction