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Title Effective Electricity Demand Prediction via Deep Learning
Authors (Daegun Ko) ; (Youngmin Yoon) ; (Jinoh Kim) ; (Haelyong Choi)
Page pp.483-489
ISSN 2287-5255
Keywords LSTM; CNN; Electricity demand prediction; Deep-learning; Machine-learning; ARIMA; MLP
Abstract Prediction of electricity demand in homes and buildings can be used to optimize an energy management system by decreasing energy wastage. A time-series prediction system is still a challenging problem in machine learning and deep learning. Our main idea is to compare three methods. For this work, we analyzed an electricity demand prediction system using the current state-of-the-art deep-learning methods with a machine-learning method: error correction with multi-layer perceptron (eMLP) structure, autoregressive integrated moving average (ARIMA) structure, and a proposed structure named CNN-LSTM. For this, we measured and collected electricity demand data in Germany for home appliances. We report the prediction accuracy in terms of the mean square error (MSE) and mean absolute percentage error (MAPE). The experimental result indicates that CNN-LSTM outperforms eMLP and ARIMA in accuracy.