Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM

Zeng, Huibin; Shao, Bilin; Dai, Hongbin; Yan, Yichuan; Tian, Ning

doi:10.1016/j.energy.2022.126125

Cited by 38 publications

(8 citation statements)

References 29 publications

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“…the predictive effect of GRU. To facilitate the comparison of prediction effects, this section also used single models to study students’ learning performance [ 32 ]. Table 5 shows the R 2 value of each model’s prediction results under the RF and FA model.…”

Section: Resultsmentioning

confidence: 99%

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network

Yin,

Tang,

Zhang

et al. 2023

PLoS ONE

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With the development of information technology construction in schools, predicting student grades has become a hot area of application in current educational research. Using data mining to analyze the influencing factors of students’ performance and predict their grades can help students identify their shortcomings, optimize teachers’ teaching methods and enable parents to guide their children’s progress. However, there are no models that can achieve satisfactory predictions for education-related public datasets, and most of these weakly correlated factors in the datasets can still adversely affect the predictive effect of the model. To solve this issue and provide effective policy recommendations for the modernization of education, this paper seeks to find the best grade prediction model based on data mining. Firstly, the study uses the Factor Analyze (FA) model to extract features from the original data and achieve dimension reduction. Then, the Bidirectional Gate Recurrent Unit (BiGRU) model and attention mechanism are utilized to predict grades. Lastly, Comparing the prediction results of ablation experiments and other single models, such as linear regression (LR), back propagation neural network (BP), random forest (RF), and Gate Recurrent Unit (GRU), the FA-BiGRU-attention model achieves the best prediction effect and performs equally well in different multi-step predictions. Previously, problems with students’ grades were only detected when they had already appeared. However, the methods presented in this paper enable the prediction of students’ learning in advance and the identification of factors affecting their grades. Therefore, this study has great potential to provide data support for the improvement of educational programs, transform the traditional education industry, and ensure the sustainable development of national talents.

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Section: Resultsmentioning

confidence: 99%

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network

Yin,

Tang,

Zhang

et al. 2023

PLoS ONE

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“…and it is interested in categorical features. It also has higher accuracy than traditional Boosting frameworks (Zeng et al, 2023).…”

Section: Categorical Boosting (Catboost)mentioning

confidence: 99%

Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms

Kahraman,

Taiwo,

Hosseini

et al. 2024

Preprint

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This study compares base, hybrid, and voting modeling techniques to predict blast toe volume size. The investigation integrates independent models, explores synergies in hybrid approaches, and optimizes accuracy through ensemble voting to offer comprehensive knowledge and more reliable forecasts for blast toe volume estimation in various design. 457 blasting was investigated and data was collected at Anguran lead and zinc mine in Iran. Nine model accuracy indices were used to compare the algorithm's prediction accuracy. The study indicates a significant relationship between toe volume size and explosive charge per delay, as demonstrated by multicollinearity, Spearman, and Kendall correlation analyses. The analysis of the model showed that Light Gradient Boosting Machine (LightGBM) achieved the highest accuracy compared to the other 8 conventional models, with correlation coefficients (R2) of 0.9004 and 0.8625 for the training and testing datasets, respectively. The Hybrid 6 model, which combines LightGBM and CART algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 model, consisting of LightGBM, GBM, DT, ET, RF, CatBoost, CART, AdaBoost, and XGBoost, had the greatest R2 scores of 0.9876 and 0.97265 in both the training and testing stages. The voting models can reliably forecast toe volume resulting from a blast design pattern, thereby providing a novel tool for simulation.

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“…The statistical techniques related to time series include pattern recognition, quadratic estimation, probabilistic modeling and weighted multimodel forecasting [5,[29][30][31] of which most focused on the impact of weather [8,9,24,32]. Regarding to time series the most used models for load forecasting are those of regression and autoregression.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

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Price spikes in electricity markets are very frequent, posing tremendous burden on household income and on manufacturing cost. Electricity demand (load) can be divided in two parts, energy (MWh) and peak (MW) and most of time peak is responsible for the price spikes. Literature review while devoting most of the discussion to energy, lags in the investigation of peak. In this research, a model for peak demand analysis and forecasting is developed. The model is based on a portfolio of cluster and extreme value analysis (C-EVA) methods using UIK, EDE and WSL innovations for the optimal determination of clusters and the daily peaks divulgence. C-EVA method consists of the Clustering part for optimal number of clusters determination and classification of day and month of peak, and the part of Extreme Value Analysis for computation of the statistical confidence interval for the load maxima. C-EVA after using all the currently available load maxima, estimates statistically the expected worst-case scenario for peaks of loads. Load peaks will be determined by EVA based on an estimated bimodal distribution while a signaling method will prompt the probability of extremes. The added value of the proposed method is that does not reject the extreme values as most methodologies do. Extreme Value Analysis for maxima and minima provide estimators for highest and lowest expected hourly load, while giving the confidence interval of the return level using an optimization method for the selection of a rolling time window, as the return period. It was found that distributed generation of renewables create a camel effect on the load peaks which increases sharpness. The proposed methodology solved this issue while opening the ground for future research for the role of storage, batteries as well as for virtual power plants as an integrated portfolio of renewables generation.

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Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM

Cited by 38 publications

References 29 publications

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network

Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

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