Respuesta de la demanda eléctrica basado en el modelo Markoviano

In this document the effects of programs demand response (DR) to be implemented in a power system are described. RD's contribution to the network is calculated using the elasticity of demand under two rate-setting mechanisms: price-based and incentive-based. A mathematical modeling is developed to determine the response sensitivity of the load to different DR programs established by the service provider, and thereby achieve an improvement in demand management. A number of prospects for the implementation of the mechanisms and applications demand response is proposed. A mathematical algorithm based on Markov models to optimize RD programs and their application in real energy markets is built, allowing to determine the probability of change of state, between different levels of electricity consumption and thus reduce peak consumption hours high demand. The objective function is formulated and the results are analyzed.En este trabajo se estudia los efectos y beneficios de los programas de Respuesta a la Demanda (RD) al ser aplicados en los sistemas eléctricos, utilizando la elasticidad del precio de la demanda en virtud de dos mecanismos: fijación dinámica de precios y de incentivos. Los resultados muestran que existe transferencia de cargas durante la aplicación RD de períodos de consumo pico a bajo consumo, obteniéndose una ciurva más plana en comparación con la curva de demanda inicial. Con los modelos Markoviano y la aplicación de procesos estocásticos se determinó en este ejemplo que la reducción y transferencia de carga es mayor con programas basados en el precio que con programas de incentivo. El trabajo de simulación se desarrolla mediante Modelos Markovianos, obteniéndose la curva de demanda dependiendo del programa de RD aplicado. Esto permite determinar cuánto sería la reducción de consumo obtenido por RD, comparando con la curva de demanda inicial del usuario. En programas en base a precio, el cliente es quien decide cuanto será el ahorro, según las tarifas fijadas en horas pico. Se realiza un análisis de los resultados obtenidos y su implementación en los mercados eléctricos

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Component-based discriminative classification for hidden Markov models

Hidden Markov models (HMMs) have been successfully applied to a wide range of sequence modeling problems. In the classification context, one of the simplest approaches is to train a single HMM per class. A test sequence is then assigned to the class whose HMM yields the maximum a posterior (MAP) probability. This generative scenario works well when the models are correctly estimated. However, the results can become poor when improper models are employed, due to the lack of prior knowledge, poor estimates, violated assumptions or insufficient training data. To improve the results in these cases we propose to combine the descriptive strengths of HMMs with discriminative classifiers. This is achieved by training feature-based classifiers in an HMM-induced vector space defined by specific components of individual hidden Markov models. We introduce four major ways of building such vector spaces and study which trained combiners are useful in which context. Moreover, we motivate and discuss the merit of our method in comparison to dynamic kernels, in particular, to the Fisher Kernel approach