scholarly journals A Graph Neural Network gas concentration prediction model based on Spatio-Temporal data

2021 ◽  
Vol 2137 (1) ◽  
pp. 012069
Author(s):  
Zhaozhao Zhang ◽  
Yuhao Ye ◽  
Lihong Dong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Temporal Data ◽  
Convolutional Network ◽  
Spatial Characteristics ◽  
Gas Concentration ◽  
Model Based ◽  
Temporal And Spatial ◽  
Concentration Prediction

Abstract For the problem of low prediction accuracy caused by traditional neural network gas concentration prediction models which did not consider temporal and spatial characteristics of gas data, this paper proposed a Spatial-Temporal graph neural network gas prediction model based on Spatial-Temporal data. Its essence was the integration of graph convolutional network and WaveNet network. In spatial dimension, graph convolutional network was used to aggregate the information of neighbor nodes, and adaptively adjusts the spatial association strength of each node according to the attention mechanism to captured the spatial characteristics of gas data. In temporal dimension, WaveNet network model was introduced, Dilated Causal Convolution was used to extract the temporal characteristics of gas data on temporal dimensions. According to the distance between gas sensors in the mine, the gas data spatial structure was constructed by Thresholded Gaussian kernel function. Experiment with the measured gas temporal and spatial data, using Mean Absolute Error (MAE) as an indicator of predictive accuracy. The experimental results show that the prediction model mentioned in this paper is significantly improved compared with the prediction accuracy of other predictive models.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

2021 ◽  
Vol 16 (4) ◽  
pp. 35-51
Author(s):  
Bingchun Liu ◽  
Xiaogang Yu ◽  
Qingshan Wang ◽  
Shijie Zhao ◽  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Maximum Temperature ◽  
Optimal Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Concentration Prediction

NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

scholarly journals Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 161 ◽  
Author(s):  
Tianjun Zhang ◽  
Shuang Song ◽  
Shugang Li ◽  
Li Ma ◽  
Shaobo Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Coal Mine ◽  
Time Series Prediction ◽  
Mean Square ◽  
Gas Concentration ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Concentration Prediction

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

scholarly journals Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)

2019 ◽  
Vol 9 (15) ◽  
pp. 2983 ◽  
Author(s):  
Jiao Liu ◽  
Guoyou Shi ◽  
Kaige Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Differential Evolution ◽  
Support Vector Regression ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Identification System ◽  
Trajectory Prediction ◽  
Model Based

There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features and the wavelet threshold de-noising method was used to process the ship position data. The adaptive chaos differential evolution (ACDE) algorithm was used to optimize the internal model parameters to improve convergence speed and prediction accuracy. AIS sensor data corresponding to a certain section of the Tianjin Port ships were selected, on which SVR, Recurrent Neural Network (RNN) and Back Propagation (BP) neural network model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on ACDE-SVR has higher and more stable prediction accuracy, requires less time and is simple, feasible and efficient.

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