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

Danxiang Wei, Jianzhou Wang, Kailai Ni and Guangyu Tang
Additional contact information
Danxiang Wei: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Jianzhou Wang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Kailai Ni: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Guangyu Tang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China

Energies, 2019, vol. 12, issue 18, 1-38

Abstract: 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).

Keywords: multi-objective grey wolf optimizer; long short-term memory; fuzzy time series; LEM2; combination forecasting; wind speed; electrical power load (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/18/3588/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/18/3588/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:18:p:3588-:d:268869

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().