A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture

Yin, Hao; Ou, Zuhong; Fu, Jiajin; Cai, Yongfeng; Chen, Shun; Meng, Anbo

doi:10.1016/j.energy.2021.121271

Cited by 70 publications

(17 citation statements)

References 36 publications

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“…The suggested IDA-SVM model outperformed the other techniques for winter and fall datasets using the R 2 , NMAE, MAPE and NRMSE error metrics. Authors in [16] suggested a new deep transfer learning strategy based on a one-of-a-kind serio-parallel CL feature extractor for multi-step forward wind power forecasting of targeted wind ranches in the absence of wealthy historical information. The findings validated the supremacy of the proposed model over the independent LSTM and CNN techniques.…”

Section: Related Workmentioning

confidence: 99%

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Tarek¹,

Shams²,

Elshewey³

et al. 2023

Computers, Materials &Amp; Continua

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Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and data preprocessing were performed to the data. The dataset used in this study includes 4 features and 50530 instances. To accurately predict the wind power values, we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization (SFS-PSO) to optimize the parameters of LSTM network. Five evaluation criteria were utilized to estimate the efficiency of the regression models, namely, mean absolute error (MAE), Nash Sutcliffe Efficiency (NSE), mean square error (MSE), coefficient of determination (R 2 ), root mean squared error (RMSE). The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R 2 equals 99.99% in predicting the wind power values.

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Section: Related Workmentioning

confidence: 99%

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Tarek¹,

Shams²,

Elshewey³

et al. 2023

Computers, Materials &Amp; Continua

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“…As a clean and pollution-free renewable resource, wind energy has attracted much attention because of its abundant resources, wide distribution and great development potential (Hua et al, 2022;Khazaei et al, 2022). However, due to the intermittent and strong variability of wind power (Yin et al, 2021;Duan et al, 2022). Therefore, it is necessary to develop a method that can accurately forecast wind power, reduce the negative impact of wind power grid connection, ensure the safe and stable operation of the power system, and improve the utilization rate of wind power in the power system (Hu et al, 2021a;Lin and Zhang, 2021;Meng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting

Zheng²,

Lu³

2023

Front. Energy Res.

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Short-term wind power forecasting plays an important role in wind power generation systems. In order to improve the accuracy of wind power forecasting, many researchers have proposed a large number of wind power forecasting models. However, traditional forecasting models ignore data preprocessing and the limitations of a single forecasting model, resulting in low forecasting accuracy. Aiming at the shortcomings of the existing models, a combined forecasting model based on secondary decomposition technique and grey wolf optimizer (GWO) is proposed. In the process of forecasting, firstly, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and wavelet transform (WT) are used to preprocess the wind power data. Then, least squares support vector machine (LSSVM), extreme learning machine (ELM) and back propagation neural network (BPNN) are established to forecast the decomposed components respectively. In order to improve the forecasting performance, the parameters in LSSVM, ELM, and BPNN are tuned by GWO. Finally, the GWO is used to determine the weight coefficient of each single forecasting model, and the weighted combination is used to obtain the final forecasting result. The simulation results show that the forecasting model has better forecasting performance than other forecasting models.

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“…Scholars in [ 22 ] investigate the superiority of transfer learning in extracting features and aim to predict the wind speed in different environments. Yin et al [ 23 ] proposed a hybrid transfer learning-based wind power forecasting model. Unfortunately, the potential relationship between statistical properties in time series and transfer learning is ignored in these works.…”

Section: Introductionmentioning

confidence: 99%

An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition

Luo

Zhang

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Smart grid is regarded as an evolutionary regime of existing power grids. It integrates artificial intelligence and communication technologies to fundamentally improve the efficiency and reliability of power systems. One serious challenge for the smart grid is its vulnerability to cyber threats. In the event of a cyber attack, grid data may be missing; subsequently, load forecast and power planning that rely on these data cannot be processed by generation centers. To address this issue, this paper proposes a transfer learning-based framework for smart grid scheduling that is less reliant on local data while capable of delivering schedules with low operating cost. Specifically, the proposed framework contains (1) a power forecasting model based on transfer learning which can provide high quality load prediction with limited training data, (2) a novel adaptive time series prediction method with modeling time series from a covariate shift perspective that aims to train the forecasting model with a strong generalization capability, and (3) a day-ahead optimal economic power scheduling model considering a shared energy storage station.

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A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture

Cited by 70 publications

References 36 publications

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Wind Power Prediction Based on Machine Learning and Deep Learning Models

A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting

An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition

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