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Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting

School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
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Energies 2019, 12(18), 3588; https://doi.org/10.3390/en12183588
Received: 5 August 2019 / Revised: 6 September 2019 / Accepted: 6 September 2019 / Published: 19 September 2019
(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)
In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error). View Full-Text
Keywords: multi-objective grey wolf optimizer; long short-term memory; fuzzy time series; LEM2; combination forecasting; wind speed; electrical power load multi-objective grey wolf optimizer; long short-term memory; fuzzy time series; LEM2; combination forecasting; wind speed; electrical power load
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Wei, D.; Wang, J.; Ni, K.; Tang, G. Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting. Energies 2019, 12, 3588.

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