에너지인터넷에서 1D-CNN과 양방향 LSTM을이용한 에너지 수요예측
Prediction for Energy Demand Using 1D-CNN and Bidirectional LSTM in Internet of Energy
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초록
에너지인터넷 기술의 발전과 다양한 전자기기의 보급으로 에너지소비량이 패턴이 다양해짐에 따라 수요예측에 대한 신뢰도가 감소하고 있어 발전량 최적화 및 전력공급 안정화에 문제를 야기하고 있다. 본 연구에서는 고신뢰성을 갖는 수요예측을위해 딥러닝 기법인 Convolution neural network(CNN)과 Bidirectional Long Short-Term Memory(BLSTM)을 융합한1Dimention-Convolution and Bidirectional LSTM(1D-ConvBLSTM)을 제안하고, 제안한 기법을 활용하여 시계열 에너지소비량대한 소비패턴을 효과적으로 추출한다. 실험 결과에서는 다양한 반복학습 횟수와 feature map에 대해서 수요를 예측하고 적은 반복학습 횟수로도 테스트 데이터의 그래프 개형을 예측하는 것을 검증한다.
Abstract
As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversifiedpatterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization ofpower generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neuralnetwork (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting byeffectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposeddeep learning method for various number of learning iterations and feature maps, and it is verified that the test data ispredicted with a small number of iterations.
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