Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction
| Autor: | Yung-Cheol Byun, Pyae Pyae Phyo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Physics and Astronomy (miscellaneous)
Mean squared error Computer science General Mathematics computer.software_genre Convolutional neural network energy consumption Computer Science (miscellaneous) forecasting accuracy QA1-939 multilayer perceptron Time series convolutional neural network (CNN) Ensemble forecasting ensemble deep learning long short-term memory (LSTM) time-series forecasting business.industry Deep learning Energy consumption Mean absolute percentage error Chemistry (miscellaneous) Multilayer perceptron Data mining Artificial intelligence business computer Mathematics |
| Zdroj: | Symmetry, Vol 13, Iss 1942, p 1942 (2021) Symmetry; Volume 13; Issue 10; Pages: 1942 |
| ISSN: | 2073-8994 |
| Popis: | The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector. |
| Databáze: | OpenAIRE |
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