A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting

Duan, Jiandong; Wang, Peng; Ma, Wentao; Fang, Shuai; Hou, Zequan

doi:10.1016/j.ijepes.2021.107452

Cited by 87 publications

(28 citation statements)

References 42 publications

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“…The performance of the real-time flood forecast improved using the ANN bias correction method. Jiandong et al [50] developed a hybrid forecasting model. In the study, the long short-term memory neural networks (LSTMs) and deep belief networks based on particle swarm optimization (PSO-DBN) were utilized to construct sub-series prediction models.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Kilinç¹

2022

Water

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Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models.

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Section: Comparative Analysis and Discussionmentioning

confidence: 99%

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Kilinç¹

2022

Water

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“… 96 VMD Decompose non-stationary series into several band-limited intrinsic mode function through VMD. 97 , 98 , 99 , 100 , 101 LMD Decompose the time series into the sum of production function components through LMD. 102 SD Combined with signal processing technologies for secondary decomposition, such as EMD-WPD, 103 , 104 FEEMD-VMD, 105 seasonal information extraction (SIE)-WD, 106 WD-VMD, 107 WPD-CEEMD with adaptive noise (CEEMDAN), 63 , 108 WPD-FEEMD, 109 VMD-EMD, 110 VMD-WPD.…”

Section: Input Data For Wind Forecastingmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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“…VMD is a variational structured signal processing method which integrates the Hilbert transform method, the alternating direction multiplier method, and the Wiener filter method [23]. More specifically, the VMD decomposition algorithm can decompose an actual signal or original data into K bandwidth-based eigenmode functions.…”

Section: Principle Of Model Construction a Variational Mode Decomposi...mentioning

confidence: 99%

Short-Term Power Load Prediction Based on VMD-SG-LSTM

Sun

Cai

2022

IEEE Access

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Power load prediction plays an important role in the safety and stability of national power system. However, due to the nonlinear and multi-frequency characteristics of the power system itself, power load prediction is difficult. To address this problem, we propose a short-term power load prediction model based on variational mode decomposition (VMD). First, original data are decomposed into intrinsic mode function (IMF) of different frequencies using the VMD algorithm, and the decomposed sub-functions are reconstructed. After smoothing the reconstructed data by Savitzky-Golay (S-G) filtering algorithm, the change trend of raw data (CTRD) is obtained. Then, IMF, CTRD and raw data are used as inputs to predict short-term power load by long short-term memory network (LSTM). Finally, the proposed prediction model is compared with the other two groups of prediction models. The results show that the proposed VMD-SG-LSTM prediction model has high fitting ability and high prediction accuracy, and is an effective method for short-term power load prediction.

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A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting

Cited by 87 publications

References 42 publications

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

An overview of deterministic and probabilistic forecasting methods of wind energy

Short-Term Power Load Prediction Based on VMD-SG-LSTM

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