Point and prediction interval estimation for electricity markets with machine learning techniques and wavelet transforms

Shrivastava, Nitin Anand; Panigrahi, Bijaya Ketan

doi:10.1016/j.neucom.2013.02.039

Cited by 20 publications

(15 citation statements)

References 41 publications

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“…El resultado es superior al obtenido por [10] en su estudio de los intervalos de confianza de las predicciones en los mercados de energía en el cual se obtuvieron PICP(%) entre 92.6 y 94.1%. El resultado obtenido es también superior al obtenido por [28] en su estudio de los intervalos de confianza de las predicciones de demanda del mercado eléctrico, los cuáles obtuvieron PICP(%) entre el 50% y el 100%.…”

Section: Discussionunclassified

Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado

2018

Rev. ECIPeru

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Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado Modeling the resistance to compression of high performance concrete test by means of an artificial neural network. Obtaining the uncertainty of the results Francisco García Fernández1, Ana Torre Carrillo2, Isabel Moromi Nakata2, Pedro Espinoza Haro3 y Luis Acuña Pinaud3 1 Dpto. Sistemas y Recursos Naturales. Universidad Politécnica de Madrid. Ciudad Universitaria S/N, 28040 Madrid, España 2 Facultad de Ingeniería Civil, Universidad Nacional de Ingeniería. Av. Túpac Amaru, 210. Lima 25, Perú 3 Facultad de Ingeniería Industrial y de Sistemas. Universidad Nacional de Ingeniería. Av. Túpac Amaru, 210. Lima 25, Perú DOI: https://doi.org/10.33017/RevECIPeru2015.0012/ Resumen En los últimos años las ANN han tenido un gran desarrollo en el control de procesos industriales debido principalmente a su capacidad de modelizar relaciones complejas, que los sistemas tradicionales no han sido capaces de hacer, y predecir satisfactoriamente si las características de un producto se adecuan o no a las especificaciones correspondientes. Estas estructuras han sido ampliamente utilizadas en la caracterización de otros materiales como cemento, hormigón, algunos metales o la madera. El perceptrón multicapa, una de las redes neuronales artificiales más populares, se ha convertido en los últimos tiempos en una potente herramienta de modelización en numerosos campos que van desde las finanzas, a la ingeniería o la medicina. Esta herramienta consigue mejorar sustancialmente cualquier modelo previo propuesto para modelizar cualquier sistema independientemente de la naturaleza de éste, con la ventaja añadida de que no necesitan ninguna suposición previa sobre la estructura de los datos Sin embargo, la red sólo proporciona el valor de la salida sin ninguna información acerca de su precisión. La obtención de la incertidumbre de salida es importante, no sólo porque proporciona un intervalo de confianza sobre el valor de salida, sino porque da una idea de la calidad del método de medida. Esta incertidumbre procede de dos fuentes, por un lado el ruido inherente a los valores de entrada y por otro la simplificación del fenómeno que todo modelo matemático supone. En este trabajo se va a desarrollar una nueva metodología para obtener tanto la incertidumbre como los intervalos de confianza de la salida de un modelo específico de red neuronal, el perceptrón multicapa, basándose en el método de simulación de Montecarlo especificado en Suplemento 1 de la GUM para posteriormente aplicarlo a la modelizacion del ensayo de resistencia a compression del concreto. Descriptores: Concreto de alta resistencia, red neuronal artificial, resistencia a compresión, incertidumbre, Método de Monte Carlo Abstract Major advances have been made with the use of ANNs in recent years in industrial process control, mainly because they are capable of modeling complex relations, unlike conventional systems, and can adequately predict whether or not the characteristics of a product are in line with specifications. They have been widely used to characterize other materials such as cement, concrete, certain metals or wood. The multilayer perceptron, one of the most popular artificial neural networks, has become a powerful modeling tool in numerous fields, ranging from finances to engineering and medicine. This tool is capable of considerably improving on all previous models proposed for modeling any system, regardless of its nature, with the added advantage that no prior assumption on the structure of the data is necessary. However, the network provides only the output value, with no information about its accuracy. Obtaining the output uncertainty is important, not only because it provides a coverage interval for the output value, but also because it indicates the quality of the measuring method. This uncertainty comes from two sources: firstly, the inherent uncertainty in the input data, and secondly, the simplification of the phenomenon involved in any mathematical model. This study develops a new methodology for obtaining both the output uncertainty and coverage intervals of a specific neural network model - the multilayer perceptron - based on the Monte Carlo simulation method indicated in Supplement 1 to the Guide to the Expression of Uncertainty in Measurement (GUM), in order to use it when modelling the test of resistance to compression of concrete. Keywords: High performance concrete, artificial neural network, resistance to compression, uncertainty, Monte Carlo method.

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Section: Discussionunclassified

Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado

2018

Rev. ECIPeru

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“…Empirical studies in the Nordic Power Pool have shown the improved electricity demand and price forecasting accuracy [22]. Shrivastava and Panigrahi [23] used the wavelet analysis as a preprocessor to improve the forecasting accuracy of the extreme learning machine in the Ontario and PJM electricity market [23]. Nguyen and Nabney [24] showed that the combination of wavelet analysis and the neural network model would result in the improved electricity demand and natural gas forecasting accuracy [24].…”

Section: Multiscale Analysis In the Energy Marketsmentioning

confidence: 99%

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Zha

Chen

et al. 2016

Entropy

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Abstract:In this paper, we propose a multiscale dependence-based methodology to analyze the dependence structure and to estimate the downside portfolio risk measures in the energy markets. More specifically, under this methodology, we formulate a new bivariate Empirical Mode Decomposition (EMD) copula based approach to analyze and model the multiscale dependence structure in the energy markets. The proposed model constructs the Copula-based dependence structure formulation in the Bivariate Empirical Mode Decomposition (BEMD)-based multiscale domain. Results from the empirical studies using the typical Australian electricity daily prices show that there exists a multiscale dependence structure between different regional markets across different scales. The proposed model taking into account the multiscale dependence structure demonstrates statistically significantly-improved performance in terms of accuracy and reliability measures.

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“…In addition, some new adaptive growth methods of hidden nodes were proposed, including AG-ELM [9] and D-ELM [10]. Apart from optimization constraints of ELM, ELM has a wide range of applications in data classification [11], nonlinear dynamic systems identification [12], pattern recognition [13][14][15], expert diagnosis [16], medical diagnosis [17], modelling permeability prediction [18], expert target recognition [19], human face recognition [20], and prediction interval estimation of electricity markets [21]. However, there are still some problems that need to be studied.…”

Section: Introductionmentioning

confidence: 99%

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

Zhao

Zhang

2018

Shock and Vibration

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An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI-ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI-ELM model, respectively. The experimental results show that the VSI-ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI-ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI-ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

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Point and prediction interval estimation for electricity markets with machine learning techniques and wavelet transforms

Cited by 20 publications

References 41 publications

Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado

Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

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