3 research outputs found
Probabilistic forecasting and interpretability in power load applications
Power load forecasting is a fundamental tool in the modern electric power generation
and distribution industry. The ability to accurately predict future behaviours of the grid,
both in the short and long term, is vital in order to adequately meet demand and scaling
requirements. Over the past few decades Machine Learning (ML) has taken center stage
in this context, with an emphasis on short-term forecasting using both traditional ML
as well as Deep-Learning (DL) models. In this dissertation, we approach forecasting not
only from the angle of improving predictive accuracy, but also with the goal of gaining
interpretability of the behavior of the electric load through models that can offer deeper
insight and extract useful information. Specifically for this reason, we focus on the use of
probabilistic models, which can shed light on valuable information about the underlying
structure of the data through the interpretation of their parameters. Furthermore, the use
of probabilistic models intrinsically provides us with a way of measuring the confidence in
our predictions through the predictive variance. Throughout the dissertation we shall focus
on two specific ideas within the greater field of power load forecasting, which will comprise
our main contributions.
The first contribution addresses the notion of power load profiling, in which ML is used
to identify profiles that represent distinct behaviours in the power load data. These profiles
have two fundamental uses: first, they can be valuable interpretability tools, as they offer
simple yet powerful descriptions of the underlying patterns hidden in the time series data;
second, they can improve forecasting accuracy by allowing us to train specialized predictive
models tailored to each individual profile. However, in most of the literature profiling
and prediction are typically performed sequentially, with an initial clustering algorithm
identifying profiles in the input data and a subsequent prediction stage where independent
regressors are trained on each profile. In this dissertation we propose a novel probabilistic
approach that couples both the profiling and predictive stages by jointly fitting a clustering
model and multiple linear regressors. In training, both the clustering of the input data
and the fitting of the regressors to the output data influence each other through a joint
likelihood function, resulting in a set of clusters that is much better suited to the prediction
task and is therefore much more relevant and informative. The model is tested on two real
world power load databases, provided by the regional transmission organizations ISO New
England and PJM Interconect LLC, in a 24-hour ahead prediction scenario. We achieve
better performance than other state of the art approaches while arriving at more consistent and informative profiles of the power load data.
Our second contribution applies the idea of multi-task prediction to the context of 24-
hour ahead forecasting. In a multi-task prediction problem there are multiple outputs that
are assumed to be correlated in some way. Identifying and exploiting these relationships can
result in much better performance as well as a better understanding of a multi-task problem.
Even though the load forecasting literature is scarce on this subject, it seems obvious to
assume that there exist important correlations between the outputs in a 24-hour prediction
scenario. To tackle this, we develop a multi-task Gaussian process model that addresses
the relationships between the outputs by assuming the existence of, and subsequently
estimating, both an inter-task covariance matrix and a multitask noise covariance matrix
that capture these important interactions. Our model improves on other multi-task Gaussian
process approaches in that it greatly reduces the number of parameters to be inferred
while maintaining the interpretability provided by the estimation and visualization of the
multi-task covariance matrices. We first test our model on a wide selection of general
synthetic and real world multi-task problems with excellent results. We then apply it to
a 24-hour ahead power load forecasting scenario using the ISO New England database,
outperforming other standard multi-task Gaussian processes and providing very useful
visual information through the estimation of the covariance matrices.La predicci贸n de carga es una herramenta fundamental en la industria moderna de la
generaci贸n y distribuci贸n de energ铆a el茅ctrica. La capacidad de estimar con precisi贸n el
comportamiento futuro de la red, tanto a corto como a largo plazo, es vital para poder
cumplir con los requisitos de demanda y escalado en las diferentes infraestructuras. A lo largo
de las 煤ltimas d茅cadas, el Aprendizaje Autom谩tico o Machine Learning (ML) ha tomado un
papel protagonista en este contexto, con un marcado 茅nfasis en la predicci贸n a corto plazo
utilizando tanto modelos de ML tradicionales como redes Deep-Learning (DL). En esta
tesis planteamos la predicci贸n de carga no s贸lo con el objetivo de mejorar las prestaciones
en la estimaci贸n, sino tambi茅n de ganar en la interpretabilidad del comportamiento de la
carga el茅ctrica a trav茅s de modelos que puedan extraer informaci贸n 煤til. Por este motivo
nos centraremos en modelos probabil铆sticos, que por su naturaleza pueden arrojar luz sobre
la estructura oculta de los datos a trav茅s de la interpretaci贸n de sus par谩metros. Adem谩s el
uso de modelos probabil铆sticos nos proporciona de forma intr铆nseca una medida de confianza
en la predicci贸n a trav茅s de la estimaci贸n de la varianza predictiva. A lo largo de la tesis
nos centraremos en dos ideas concretas en el contexto de la predicci贸n de carga el茅ctrica,
que conformar谩n nuestras aportaci贸nes principales.
Nuestra primera contribuci贸n plantea la idea del perfilado de la carga el茅ctrica, donde
se utilizan modelos de ML para identificar perfiles que representan comportamientos
diferenciables en los datos de carga. Estos perfiles tienen dos usos fundamentales: en
primer lugar son herramientas 煤tiles para la interpretabilidad del problema ya que ofrecen
descripciones sencillas de los posibles patrones ocultos en los datos; en segundo lugar,
los perfiles pueden ser utilizados para mejorar las prestaciones de estimaci贸n, ya que permiten entrenar varios modelos predictivos especializados en cada perfil individual. Sin
embargo, en la literatura el perfilado y la predicci贸n se presentan como eventos en cascada,
donde primero se entrena un algoritmo de cl煤stering para detectar perfiles que luego son
utilizados para entrenar los modelos de regresi贸n. En esta tesis proponemos un modelo
probabil铆stico novedoso que acopla las dos fases ajustando simult谩neamente un modelo
de cl煤stering y los correspondientes modelos de regresi贸n. Durante el entrenamiento
ambas partes del modelo se influencian entre s铆 a trav茅s de una funci贸n de verosimilitud
conjunta, resultando en un conjunto de clusters que est谩 mucho mejor adaptado a la tarea
de predicci贸n y es por tanto mucho m谩s relevante e informativo. En los experimentos, el
modelo es entrenado con datos reales de carga el茅ctrica provinientes de dos bases de datos
p煤blicas proporcionadas por las organizaci贸nde de transmisi贸n regional estadounidenses
ISO New England y PJM Interconect LLC, en un escenario de predicci贸n a 24 horas. El
modelo obtiene mejores prestaciones que otros algoritmos competitivos, proporcionando al
mismo tiempo un conjunto de perfiles del comportamiento de la carga m谩s consistente e
informativo.
Nuestra segunda contribuci贸n aplica la idea de predicci贸n multi-tarea al contexto de
la estimaci贸n a 24 horas. Los problemas multi-tarea presentan m煤ltiples salidas que se
asume est谩n de alguna forma correladas entre s铆. Identificar y aprovechar estas relaciones
puede incurrir en un incremento de las prestaciones as铆 como un mejor entendimiento del
problema multi-tarea. A pesar de que la literatura de predicci贸n de carga es escasa en este
sentido, parece l贸gico pensar que deben existir importantes correlaciones entre las salidas
de un escenario de predicci贸n a 24 horas. Por este motivo hemos desarrollado un proceso
Gaussiano multi-tarea que recoge las relaciones entre salidas asumiendo la existencia de de
una covarianza inter-tarea as铆 como un ruido multi-tarea. Nuestro modelo ofrece mejoras
con respecto a otras formulaciones de procesos Gaussianos multi-tarea al reducir el n煤mero
de par谩metros a estimar mientras se mantiene la interpretabilidad proporcionada por la
estimaci贸n y visualizacion de las matrices de covarianza y ruido inter-tarea. Primero, en la
fase de experimentos nuestro modelo es puesto a prueba sobre una bater铆a de bases de datos
tanto sint茅ticas como reales, obteniendo muy buenos resultados. A continuaci贸n se aplica
el modelo a un problema de predicci贸n de carga a 24 horas utilizando la base de datos
de ISO New England, batiendo en prestaciones a otros procesos Gaussianos multi-tarea y
proporcionando informaci贸n visual 煤til mediante la estimaci贸n de las matrices de covarianza
inter-tarea.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Pablo Mart铆nez Olmos.- Secretario: Pablo Mu帽oz Moreno.- Vocal: Jos茅 Palacio
Forecast-informed power load profiling: A novel approach
Power load forecasting plays a critical role in the context of electric supply optimization. The concept ofload characterization and profiling has been used in the past as a valuable approach to improve forecasting performance as well as problem interpretability. This paper proposes a novel, fully fledged theoretical framework for a joint probabilistic clustering andregression model, which is different from existing models that treat both processes independently. The clustering process is enhanced by simultaneously using the input data and the prediction targets during training. The model is thus capable of obtaining better clusters than other methods, leading to more informativedata profiles, while maintaining or improving predictive performance. Experiments have been conducted using aggregated load data from two U.S.A. regional transmission organizations, collected over 8 years. These experiments confirm that the proposed model achieves the goalsset for interpretability and forecasting performance.This work is partially supported by the National Science Foundation EPSCoR Cooperative Agreement OIA-1757207 and the SpanishMINECO grants TEC2014-52289-R and TEC2017-83838-R
Evaluation of dimensionality reduction methods applied to numerical weather models for solar radiation forecasting
The interest in solar radiation prediction has increased greatly in recent times among the scientific community. In this context, Machine Learning techniques have shown their ability to learn accurate prediction models. The aim of this paper is to go one step further and automatically achieve interpretability during the learning process by performing dimensionality reduction on the input variables. To this end, three non standard multivariate feature selection approaches are applied, based on the adaptation of strong learning algorithms to the feature selection task, as well as a battery of classic dimensionality reduction models. The goal is to obtain robust sets of features that not only improve prediction accuracy but also provide more interpretable and consistent results. Real data from the Weather Research and Forecasting model, which produces a very large number of variables, is used as the input. As is to be expected, the results prove that dimensionality reduction in general is a useful tool for improving performance, as well as easing the interpretability of the results. In fact, the proposed non standard methods offer important accuracy improvements and one of them provides with an intuitive and reduced selection of features and mesoscale nodes (around 10% of the initial variables centered on three specific nodes).This work has been partially supported by the projects TIN2014-54583-C2-2-R, TEC2014-52289-R and TEC2016-81900-REDT of the Spanish Interministerial Commission of Science and Technology (MICYT), and by Comunidad Aut贸noma de Madrid, under project PRICAM P2013ICE-2933