KEYWORDS: Performance modeling, Data modeling, Deep learning, Transformers, Machine learning, Feature extraction, Education and training, Convolution, Temperature metrology, Data conversion
Accurate electricity price forecasting is of great importance to all participants in the market, which can provide powerful support for them to make wise decisions in the unpredictable market. In this paper, we propose to use the MLP-Mixer model as a new technique for electricity price forecasting. This model considers the various factors affecting electricity price fluctuations and can use the MLP-Mixer model for concise and practical feature extraction and information interaction, ultimately achieving more accurate electricity price forecasting. The effectiveness of the proposed model is verified by using data from ERCOT, Texas electricity market. Empirical results show that the proposed model can significantly improve the forecasting accuracy, and achieves the optimal results on four evaluation metrics, which fully demonstrates the model's effectiveness for electricity price forecasting.
In this paper, we propose a bi-level structured classifier integrating unsupervised and supervised machine learning models, which aims to improve the model's decision-making ability on classification boundaries by dividing the sample subspace to make full use of the multivariate attribute features and spatial structure of the data. The bi-level structured classifier utilizes the unsupervised clustering algorithms for subspace partitioning of sample data in the first layer, and selects the applicable supervised models to learn on the subspace samples in the second layer. We conduct a case study on a lithology dataset from the complex carbonate reservoirs for lithology identification. The classification results indicate that the bi-level integrated classifier (98.77%) is superior to the machine learning models (XGBoost: 97.67 %). And the ability of the bi-level integrated architecture is verified in effectiveness and generalization, and effectively improves the classification performance.
In this paper, we propose a lightweight machine learning (ML) framework based on unsupervised spectral domain discretization for hyperspectral image (HSI) classification. Firstly, the high-dimensional HSI data is mapped into a discretized image by unsupervised learning method, and then the histogram statistics of discrete features are performed to align feature vectors. Finally, supervised ML method is used for classification, thus achieving a lightweight ML method of high-dimensional HSIs. Practical applications and comparative studies on three publicly available HSI datasets show that the framework approaches and surpasses deep learning models in classification accuracy while significantly compressing computational time consumption. The performance of six unsupervised clustering methods in HSI spectral domain discretization is compared in the study. Among them, K-means and GMM are superior in terms of classification accuracy. And SOM provides high classification accuracy while its discretization results are better interpretable due to better maintenance of topology during discretization.
Shear wave (S-wave) velocity prediction is important for the evaluation of shale oil and gas reservoir. However, there are some problems with traditional models: the parameters of the petrophysical model are relatively fixed, and the machine learning models do not consider the sequence information of the log data. Therefore, the S-wave velocity prediction model based on Temporal Convolutional Network (TCN) for shale reservoir is proposed. The model can flexibly extract the sequence features by adopting causal convolution and dilation factors and mine the inner relationship between the well logs and the reservoir S-wave velocity to achieve a better prediction performance. Two wells of MY1 and FN4 in shale reservoir in the Permian Fengcheng Formation in Mahu Sag of Junggar Basin, Xinjiang Oilfield are taken as an example. The TCN model achieves optimal results on both MY1 and FN4 with mean relative error (MRE) of 0.84% and 1.39%, respectively, when compared with the results of traditional petrophysical models, machine learning models and conventional deep learning models. This indicates that the TCN model has strong effectiveness and generalization in Swave velocity prediction, which provides a new idea for S-wave velocity prediction in shale reservoir.
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