GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Sanz-García, Andrés; Ceniceros, Julio Fernández; Antoñanzas-Torres, F.; Pernía‐Espinoza, Alpha; Martínez-de-Pisón, Francisco Javier

doi:10.1016/j.asoc.2015.06.012

Cited by 47 publications

(6 citation statements)

References 34 publications

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“…In order to select the best structure of the forecasting model, an optimization methodology was used based on the genetic algorithm (GA) with advanced generalization capabilities. This methodology is the GA-PARSIMONY [71], which allows the selection of parsimonious models. The main difference of this methodology with respect to the conventional GAs is a rearrange in the ranking of the individuals based on their complexities, so that individuals with less complexity (in this case, models with a less complex structure) are promoted to the best position of each generation.…”

Section: Prediction Results For the Photovoltaic Generationmentioning

confidence: 99%

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

et al. 2019

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The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.

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Section: Prediction Results For the Photovoltaic Generationmentioning

confidence: 99%

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

et al. 2019

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“…In recent years, there have been many methods for optimizing the model parameters of the SVR algorithm (Sanz‐Garcia et al, 2015). Typically, gradient descent or grid algorithms have been used, but these traditional optimization methods are too slow in the training process, and it is easy to return to the local optimal solution (Moshkbar‐Bakhshayesh, 2019).…”

Section: Discussionmentioning

confidence: 99%

Understanding the influencing factors and mechanisms (land use changes and check dams) controlling changes in the soil organic carbon of typical loess watersheds in China

et al. 2022

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Studying the distribution patterns and controlling mechanisms of soil organic carbon (SOC) based on the comprehensive performance of vegetation restoration and check dams at the watershed scale is important for understanding carbon cycling processes in nature. Two typical watersheds (Xinshui River and Zhujiachuan watershed) of the Loess Plateau were selected to evaluate the factors affecting the change in SOC content, and then the key factors were considered in the genetic algorithm‐support vector regression (GA‐SVR) model to predict SOC content. The results showed that the topography, vegetation, and soil characteristics had significant effects on the SOC content in the upland hillslopes, while the SOC content in the check dams was significantly affected by depth and soil characteristics. The soil organic carbon storage (TSOC) in the check dams could be evaluated and predicted by the vegetation index (NGRDI) and area of the subwatershed. The GA‐SVR model had good prediction accuracy and stable performance in predicting SOC content. According to the model simulation results, bulk density (BD), mean weight diameter (MWD), elevation, NGRDI, clay ratio (CR), and slope could be used to predict the surface SOC content of the Loess Plateau. Furthermore, depth, CR, MWD, BD, and median particle size (D50) could be applied in the model to predict the SOC content at different depths in the check dams. This study explored the potential control factors of SOC content and predicted SOC content from multiple angles, which can provide basic support for the study of the carbon sequestration on the Loess Plateau.

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“…First, a GP (with UCB as an acquisition function) is used to obtain the best HPO setting (according to the RMSE), considering the full set of features. Next, a variant of GA (GA-PARSIMONY, Sanz-García et al 2015) is used to select the best features of the problem, given the hyperparameters obtained in the first step. In this way, the final model has high accuracy and lower complexity (i.e., fewer features), and optimization time is significantly reduced.…”

Section: Hybrid Hpo Algorithmsmentioning

confidence: 99%

A survey on multi-objective hyperparameter optimization algorithms for machine learning

2022

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Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared that focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.

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GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Cited by 47 publications

References 34 publications

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

Understanding the influencing factors and mechanisms (land use changes and check dams) controlling changes in the soil organic carbon of typical loess watersheds in China

A survey on multi-objective hyperparameter optimization algorithms for machine learning

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