Waterfowl breeding environment humidity prediction based on the SRU-based sequence to sequence model

Chen, Yaocong; Fan, Majie; Hassan, Shahbaz Gul; Zhou, Bing; Fan, Wei; Li, Jingbin; Liu, Tonglai; Liu, Shuangyin; Wu, Huilin; Xu, Longqin

doi:10.1016/j.compag.2022.107271

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Additionally, it has better long-period acquisition and nonlinear processing capability than TCNs. The SRU network has recently been applied to complex nonlinear classification and regression problems, achieving commendable predictive results in water quality prediction [32], humidity in waterfowl breeding environments [33], remaining useful life prediction of bearings [34], and spatiotemporal traffic speed prediction in urban road networks [35].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Tang,

Zhang,

Huang

et al. 2024

Applied Sciences

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In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a Simple Recurrent Unit (SRU), K-Nearest Neighbors (KNN), and Hunter–Prey Optimization (HPO). Firstly, the sliding time window is determined by using the autocorrelation function (ACF), partial correlation function (PACF), and model training. The Pearson correlation coefficient method is used to filter the principal meteorological factors that affect photovoltaic power. Then, the K-Nearest Neighbors (KNN) algorithm is utilized for effective outlier detection and processing to ensure the quality of input data for the prediction model, and the Hunter–Prey Optimization (HPO) algorithm is applied to optimize the parameters of the KNN algorithm. Finally, the efficient Simple Recurrent Unit (SRU) model is used for training and prediction, with the Hunter–Prey Optimization (HPO) algorithm applied to optimize the parameters of the SRU model. Simulation experiments and extensive ablation studies using photovoltaic data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, validate the effectiveness of the integrated model, the KNN outlier handling, and the HPO algorithm. Compared to the Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Simple Recurrent Unit (SRU) models, this model exhibits an average reduction of 19.63% in Mean Square Error (RMSE), 27.54% in Mean Absolute Error (MAE), and an average increase of 1.96% in coefficient of determination (R2) values.

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Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Tang,

Zhang,

Huang

et al. 2024

Applied Sciences

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Deep reinforcement learning algorithm based ramp merging decision model

Chen,

Du,

Jiang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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On-ramp merge is a complex traffic scenario in autonomous driving. Because of the uncertainty of the driving environment, most rule-based models cannot solve such a problem. This paper designs a ramp merging decision model based on deep deterministic policy gradient algorithm (DDPG) to solve the vehicle merging problem. To address the problems of slow algorithm merging and poor robustness of previous deep reinforcement learning algorithms in the field of intelligent vehicle ramp merging leading to the low success rate of intelligent vehicle merging, first, we introduce a simple recurrent unit (SRU) for extracting intelligent vehicle states and environment features and use the DDPG algorithm for intelligent vehicle decision making. Second, the experience playback pool of DDPG algorithm is improved by using priority sampling instead of uniform sampling. Finally, a multi-objective reward function is set up during training, considering factors such as safety and efficiency. The simulation experiments show that the improved algorithm improves the merging speed of the model, reduces the collision rate, and enables the vehicle to make more reasonable decisions. In addition, the superiority of the method is demonstrated by comparing with the advanced method.

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Waterfowl breeding environment humidity prediction based on the SRU-based sequence to sequence model

Cited by 2 publications

References 21 publications

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Deep reinforcement learning algorithm based ramp merging decision model

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