A Hybrid Model Based on Symbolic Regression and Neural Networks for Electricity Load Forecasting

Dimoulkas, Ilias; Herre, Lars; Khastieva, Dina; Nycander, Elis; Amelin, Mikael; Mazidi, Peyman

doi:10.1109/eem.2018.8469901

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…The training data set is used to generate and optimize solutions whereas the validation set is used for model selection, that is, for identifying models for the final Pareto front of solutions. During the models' searching process, the program constructs a set of different models whose progress and performance over time can be assessed in a Pareto front (21), as shown in Figure 3, allowing the evaluation of the fitness score of the validation set and the complexity of the model. Eureqa applies a penalty proportional to the formula's complexity to avoid overfitting.…”

Section: Description Of Regression Methodsmentioning

confidence: 99%

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

Camarena

2021

Transportation Research Record: Journal of the Transportation R

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The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurement of the material properties from laboratory testing, whereas other levels make use of equations developed from regression models to estimate the material properties. Resilient modulus is a mechanical property that characterizes the unbound and subgrade materials under loading that is essential for the mechanistic design of pavements. The MEPDG resilient modulus model makes use of a three-parameter constitutive model to characterize the nonlinear behavior of the geomaterials. As the resilient modulus tests are complex, expensive, and require lengthy preparation time, most state highway agencies are unlikely to implement them as routine daily applications. Therefore, it is imperative to make use of models to calculate these nonlinear parameters. Existing models to determine these parameters are frequently based on linear regression. With the development of machine learning techniques, it is feasible to develop simpler equations that can be used to estimate the nonlinear parameters more accurately. This study makes use of the Long-Term Pavement Performance database and machine learning techniques to improve the equations utilized to determine the nonlinear parameters crucial to estimate the resilient modulus of unbound base and subgrade materials.

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Section: Description Of Regression Methodsmentioning

confidence: 99%

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

Camarena

2021

Transportation Research Record: Journal of the Transportation R

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“…Interestingly, genetic algorithms have been used in various econometric models, and were claimed to be very useful (Claveria et al 2022;Garcia and Kristjanpoller 2019;Claveria et al 2016Claveria et al , 2017Mostafa and El-Masry 2016;Aguilar-Rivera et al 2015;Sermpinis et al 2015;Sheta et al 2013;Hasheminia and Niaki 2006). While widely popular in technical fields, such as engineering, and nature-oriented sciences, such as ecology and medicine (Dimoulkas et al 2018;Klotz et al 2017;Golafshani and Ashour 2016;Ceperic et al 2014;Narotam et al 2014;Sarradj and Geyer 2014), they have not yet been extensively applied in economics or finance, especially in the context of variable uncertainty, and particularly for forecasting commodity prices.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Drachal,

Pawłowski

2024

IJFS

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This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed.

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“…En cuanto a la predicción del consumo eléctrico, entre los trabajos basados en Regresión Simbólica, (Jinliang Yin et al, 2008) utiliza la variante Gene Expression Programming, pero sólo tienen en cuenta las variables consumo eléctrico y temperatura como entradas. Por otro lado, (Dimoulkas et al, 2018) utiliza Programación Genética sólo para seleccionar variables de entrada, mientras que el proceso de predicción se realiza con redes neuronales. Entonces, no se tiene un modelo matemático explícito que pueda ser analizado posteriormente.…”

Section: Introductionunclassified

Regresión Simbólica aplicada a la Predicción del Consumo Eléctrico a Corto Plazo en el Nivel de Subestación

Jimenez

Lescano

Will

2020

RTyC

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El modelado de datos es un problema fundamental en diversas áreas del conocimiento. La Regresión Simbólica es una técnica que permite encontrar una relación matemática para describir un conjunto de datos experimentales. A diferencia de los métodos tradicionales de modelado, la Programación Genética permite encontrar una expresión matemática susceptible de ser analizada e interpretada. Multi-Expression Programming es una variante de Programación Genética, que presenta múltiples ventajas, haciéndola apta para su uso en casos reales. En este trabajo proponemos aplicar dicha variante para descubrir modelos de predicción, un día hacia adelante, del consumo eléctrico de subestación transformadora en Tucumán, Argentina. Para analizar el comportamiento del algoritmo y ajustar parámetros, se realizaron pruebas utilizando Benchmarks conocidos. Se concluye que Multi-Expression Programming es adecuada para encontrar modelos en problemas complejos, y en el caso de predicción de consumo eléctrico se logró un nivel de error similar al obtenido con otras técnicas.

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A Hybrid Model Based on Symbolic Regression and Neural Networks for Electricity Load Forecasting

Cited by 5 publications

References 14 publications

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Regresión Simbólica aplicada a la Predicción del Consumo Eléctrico a Corto Plazo en el Nivel de Subestación

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