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Traffic flow prediction based on BILSTM model and data denoising scheme

Chinese Physics B 2022年第4期31卷 191-200页

作者： Zhong-Yu Li Hong-Xia Ge Rong-Jun Cheng Faculty of Maritime and Transportation Ningbo UniversityNingbo 315211China Jiangsu Provincial Collaborative Innovation Center for Modern Urban Traffic Technologies Nanjing 210096China National Traffic Management Engineering and Technology Research Center Ningbo University Subcenter Ningbo 315211China

Accurate prediction of road traffic flow is a significant part in the intelligent transportation *** prediction can alleviate traffic congestion,and reduce environmental *** the management department,it can make effective use of road *** individuals,it can help people plan their own travel paths,avoid congestion,and save *** to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain *** the influence of noise on traffic flow prediction is a piece of very important ***,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow *** the same time,three denoising algorithms are compared to find the best combination *** this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow *** addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic *** data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic *** results for mainline show that data denoising can improve prediction ***,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for *** is indicated that this model is suitable for different road sections and long-term prediction.

关键词： traffic flow prediction bidirectional long short-term memory network data denoising

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Hybrid Short-term Load Forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1216-1228页

作者： Xiaoyu Zhang Stefanie Kuenzel Nicolo Colombo Chris Watkins School of Artificial Intelligence Anhui UniversityHefei 230601China Department of Electronic Engineering Royal HollowayUniversity of LondonEgham HillEgham TW20 OEXUK Department of Computer Science Royal HollowayUniversity of LondonEgham HillEgham TW200EXUK

Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression(LR), auto-regressive integrated moving average(ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform(EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory(LSTM) method is trained for each sub-layer independently. In order to better tune the hyperparameters, a Bayesian hyperparameter optimization(BHO) algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed *** the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

关键词： Load forecasting empirical wavelet transform(EWT) recurrent neural network data denoising Bayesian hyperparameter optimization(BHO)

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