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Danxiang Wei
School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China

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Journal article
Published: 13 April 2021 in Applied Energy
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Deep recurrent neural networks, such as gated recurrent units and long short-term memories, have been widely applied in wind speed forecasting. However, the simulations of the dynamics of the neurons in these models are different from the dynamics of natural neurons, and the useful temporal information is not fully extracted. This results in an unsatisfactory forecasting accuracy for practical wind energy management. In this study, under the hypothesis that a wind speed series can be forecasted using only previous observations (without any other information from the outer environment), a hybrid dual temporal information wind speed forecasting system comprising a third-generation spiking neural network is proposed, aiming to better extract temporal information. A fluctuating feature decomposition strategy is adopted to separate the different modes and adaptively transform the original series into several subseries. Subsequently, the third-generation spiking neural network is integrated with a convolution operation to correct and optimize the forecasting performance of a single recurrent deep learning model. Finally, an effective optimization algorithm is applied to obtain a linear combination of the forecasting outputs of each subseries. Four wind datasets collected from the Liaotung Peninsula in China are used to verify the effectiveness of the designed forecasting system. The experiments indicate that the proposed forecasting system achieves MAPEhengshan=1.43%, MAPExianren=1.40%, MAPEdonggang=1.49%, and MAPEdandong=2.56%, thereby showing excellent forecasting performance.

ACS Style

Danxiang Wei; Jianzhou Wang; Xinsong Niu; Zhiwu Li. Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks. Applied Energy 2021, 292, 116842 .

AMA Style

Danxiang Wei, Jianzhou Wang, Xinsong Niu, Zhiwu Li. Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks. Applied Energy. 2021; 292 ():116842.

Chicago/Turabian Style

Danxiang Wei; Jianzhou Wang; Xinsong Niu; Zhiwu Li. 2021. "Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks." Applied Energy 292, no. : 116842.

Journal article
Published: 19 September 2019 in Energies
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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).

ACS Style

Danxiang Wei; Jianzhou Wang; Kailai Ni; Guangyu Tang; Wei; Wang; Ni; Tang. 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 .

AMA Style

Danxiang Wei, Jianzhou Wang, Kailai Ni, Guangyu Tang, Wei, Wang, Ni, Tang. 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 (18):3588.

Chicago/Turabian Style

Danxiang Wei; Jianzhou Wang; Kailai Ni; Guangyu Tang; Wei; Wang; Ni; Tang. 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting." Energies 12, no. 18: 3588.

Journal article
Published: 26 June 2019 in Energies
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Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

ACS Style

Kailai Ni; Jianzhou Wang; Guangyu Tang; Danxiang Wei. Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia. Energies 2019, 12, 2467 .

AMA Style

Kailai Ni, Jianzhou Wang, Guangyu Tang, Danxiang Wei. Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia. Energies. 2019; 12 (13):2467.

Chicago/Turabian Style

Kailai Ni; Jianzhou Wang; Guangyu Tang; Danxiang Wei. 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia." Energies 12, no. 13: 2467.