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Deep learning driven data analytics for smart grids
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAs advanced metering infrastructure (AMI) and wide area monitoring systems (WAMSs) are being deployed rapidly and widely, the conventional power grid is transitioning towards the smart grid at an increasing speed. A number of smart metering devices and real-time monitoring systems are capable to generate a huge volume of data on a daily basis. However, a variety of generated data can be made full use of to advance the development of the smart grid through big data analytics, especially, deep learning. Thus, the thesis is focused on data analysis for smart grids from three different aspects.
Firstly, a real-time data driven event detection method is presented, which is quite robust when dealing with corrupted and significantly noisy data of phase measurement units (PMUs). To be specific, the presented event detection method is based on a novel combination of random matrix theory (RMT) and Kalman filtering. Furthermore, a dynamic Kalman filtering technique is proposed through the adjustment of the measurement noise covariance matrix as the data conditioner of the presented method in order to condition PMU data. The experimental results show that the presented method is indeed quite robust in such practical situations that include significant levels of noisy or missing PMU data.
Secondly, a short-term residential load forecasting method is proposed on the basis of deep learning and k-means clustering, which is capable to extract similarity of residential load effectively and perform prediction accurately at the individual residential level. Specifically, it makes full use of k-means clustering to extract similarity among residential load and deep learning to extract complex patterns of residential load. In addition, in order to improve the forecasting accuracy, a comprehensive feature expression strategy is utilised to describe load characteristics of each time step in detail. The experimental results suggest that the proposed method can achieve a high forecasting accuracy in terms of both root mean square error (RMSE) and mean absolute error (MAE).
Thirdly, an online individual residential load forecasting method is developed based on a combination of deep learning and dynamic mirror descent (DMD), which is able to predict residential load in real time and adjust the prediction error over time in order to improve the prediction performance. More specifically, it firstly employs a long short term memory (LSTM) network to build a prediction model offline, and then applies it online with DMD correcting the prediction error. In order to increase the prediction accuracy, a comprehensive feature expression strategy is used to describe load characteristics at each time step in detail. The experimental results indicate that the developed method can obtain a high prediction accuracy in terms of both RMSE and MAE.
To sum up, the proposed real-time event detection method contributes to the monitoring and operation of smart grids, while the proposed residential load forecasting methods contribute to the demand side response in smart grids.TDX-ASSIS
Reconocimiento de voz a través de técnicas hÃbridas utilizando modelos Markovianos y nuevos tipos de redes neuronales
The speech recognition module within a spoken dialogue system has become
a key factor over time. The improvements that can be made with the new approaches
and techniques have shown the evolutionary path that can be carried out in
many processes of training and architecture definition in order to obtain superior
recognition rates. In this sense, the present research has as objective to investigate
new schemes to improve the word error rates (WER). The present work is based
on the idea of using the deep neural networks and hidden Markov models (DNNHMM)
architecture, which relies heavily on the behavior of the Gaussian mixture
models and hidden Markov models (GMM-HMM) approach. First, experimental
comparisons are made taking into consideration both approaches. The research
process has been performed by using a corpus of personalized voices in Spanish
from the northern central part of Mexico, based on a connected-words phone
dialing task through the recognition of digit strings and personal name lists. The
specified recognition task is defined as speaker-independent, text-dependent and
mid-vocabulary. In the first experimental case study, a relative improvement of
30% was obtained using the acoustic model based on neural networks (WER
of 1:49%), compared to the classic acoustic model based on Gaussian mixtures
(2:12%). In the second case study, a relative improvement of 20:71% was achieved
with the connectionist approach (neural networks, WER of 3:33%) with regard to
the Gaussian mixture model (4:20%). The presented recognition task shows that
the current approaches based on connectionist models, originated in artificial
intelligence, surpass the traditional approaches of Gaussian mixtures in most
of the speech recognition tasks. With the purpose of obtaining improvements in the recent speech recognition models, the second part of the thesis proposes new
cost functions to train a neural network, calling these functions as non-uniform
mapped criteria. These functions allow superior recognition rates in comparison
with the conventional cross-entropy function within the training of a deep neural
network, by using the back-propagation algorithm and an optimization with
the gradient descent procedure. The obtained results (a relative improvement of
12:3% and 10:7% was achieved with the two proposed approaches, with respect
to the conventional model of cross-entropy) have shown improvements in the
word error rates, suggesting that the proposed cost functions have arguments to
be considered as interesting alternatives in this type of tasks. Nevertheless, we
must continue with the work of testing this and new cost function mechanisms
with different voice corpus in several conditions with and without environmental
noise, in addition to considering radical variations in the speakers’ speech
sources.El módulo de reconocimiento de voz dentro de un sistema de dialogo hablado
se ha convertido en un punto clave con el paso del tiempo. Las mejoras que
se le pueden hacer con los nuevos enfoques y técnicas han mostrado el camino
evolutivo que se puede dar en muchos procesos de entrenamiento y definición
de arquitecturas con el fin de obtener mejores tasas de reconocimiento. En este
sentido, el presente trabajo tiene como objetivo investigar esquemas que permitan
mejorar las tasas de error por palabra (WER). El trabajo se fundamenta en
la idea del uso de la arquitectura de red neuronal profunda y modelos ocultos
de Markov (RNP-MOM), la cual se basa en gran medida en el comportamiento
del enfoque de modelo de mezclas Gaussianas y modelos ocultos de Markov
(MMG-MOM). En primera instancia se hacen comparaciones experimentales en
el funcionamiento de ambos enfoques tomando como punto de partida un corpus
de voces personalizado en Español de la parte norte central de México, basado en
una tarea de marcado telefónico a través de reconocimiento de dÃgitos numéricos
y nombres completos de personas, con independencia de locutor, con dependencia
de texto, de tamaño mediano y con palabras conectadas. En el primer caso
de estudio experimental se obtuvo una mejora relativa del 30% usando el modelo
acústico de redes neuronales (WER de 1:49%), en comparación con el modelo clásico
de mezclas Gaussianas (2:12%). En el segundo caso de estudio se consiguió
una mejora relativa de 20:71% en la tasa de error por palabras del enfoque conexionista
(redes neuronales, WER de 3:33%) con respecto al modelo de mezclas
Gaussianas (4:20%). En las tareas de reconocimiento presentadas se muestra que
los enfoques actuales cimentados en modelos conexionistas, con origen en la inteligencia artificial, superan en la mayorÃa de los procesos de reconocimiento a
los enfoques tradicionales de mezclas Gaussianas. Con el fin de conseguir mejoras
en los modelos recientes de reconocimiento de voz, en la segunda parte del
trabajo se proponen nuevas funciones de costo para entrenar una red neuronal,
denominando a estas funciones como mapeadas no uniformes. Estas funciones
permiten obtener mejores tasas de reconocimiento en comparación con la función
convencional de entropÃa cruzada dentro del entrenamiento de una red neuronal
profunda, utilizando para ello el algoritmo de retro-propagación y una optimización
con el gradiente descendente. Los resultados obtenidos (se consiguió una
mejora relativa de 12:3% y 10:7% con los dos enfoques planteados, con respecto
al modelo base de entropÃa cruzada) han mostrado mejoras en las tasas de error
por palabra, sugiriendo que las funciones de costo propuestas tienen argumentos
para ser consideradas como alternativas interesantes en este tipo de tareas.
No obstante, se debe seguir en la labor de probar este y nuevos mecanismos de
función de costo con diferentes corpus de voces y en diversos entornos con y sin
ruido ambiental, además de considerar variaciones radicales en los origenes de
voz de los locutores