Reactive Power Optimization Approach Based on Chaotic Particle Swarm Optimization

Li, Fengqiang; Song, Lianjun; Cong, Bo

doi:10.1007/978-981-15-3383-9_12

Cited by 1 publication

(1 citation statement)

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“…At present, many experts and scholars at home and abroad have made a lot of meaningful work in the direction of gas concentration prediction, such as the establishment of gas concentration prediction methods based on multivariate distribution lag, support vector machines, − BP (back propagation) neural networks, − and other models, all of which have achieved good prediction results. However, with the popularization of coal mine monitoring and surveillance systems and the rapid development of big data mining and artificial intelligence in recent years, coal mining enterprises have accumulated huge amounts of gas time series data; recurrent neural networks in deep learning are also very good at solving such serial data problems; and a series of related studies have emerged.…”

Section: Introductionmentioning

confidence: 99%

Multiple Sequence Long and Short Memory Network Model for Corner Gas Concentration Prediction on Coal Mine Workings

et al. 2022

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To further improve the accuracy of recurrent neural network in predicting the gas concentration in the upper corner of the mine tunnel, this paper proposes a method to construct a gas concentration prediction model based on multiple sequence long and short memory network, considering the spatial correlation between the gas concentration in the return airway and upper corner. The reliability of the model construction is improved by using the white noise test and smoothness test to verify the interpretability of the data in this paper and constructing supervised learning type data for gas concentration prediction model training and testing by means of data set division and data windowing. Through experimental comparison, grid search, and time series decomposition, the model algorithm, training parameters, and experimental results were combined to make an in-depth analysis of the influence of each parameter on the model training and the prediction. A training model of the spatially fused gas concentration prediction model with a network layer of 1 and a number of neurons of 32 as the model structure, Adam as the optimization algorithm, and a learning rate of 0.001 and a batch size of 32 as the training parameters was finally determined. The gas concentration prediction model trained in this paper performed well in the test set with a mean square error (MSE) of 0.0013, and its superiority was verified by comparing it with other models to provide some experience and basis for subsequent studies on gas concentration prediction in the upper corner.

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Section: Introductionmentioning

confidence: 99%