Advances in Electric Power and Energy Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Press Series on Power Engineering
• Sprache: Englisch

Seitenzahl: 514

Veröffentlichungsjahr: 2017

Beschreibung

A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally

Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more.

In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial arenas. Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every maximization strategy. This book fills a gap in the literature on this increasingly important topic.

Following an introductory chapter offering background information necessary for a full understanding of the forecasting issues covered, this book: 

• Introduces advanced methods of time series forecasting, as well as neural networks
• Provides in-depth coverage of state-of-the-art power system load forecasting and electricity price forecasting 
• Addresses river flow forecasting based on autonomous neural network models
• Deals with price forecasting in a competitive market
• Includes estimation of post-storm restoration times for electric power distribution systems
• Features contributions from world-renowned experts sharing their insights and expertise in a series of self-contained chapters

Advances in Electric Power and Energy Systems is a valuable resource for practicing engineers, regulators, planners, and consultants working in or concerned with the electric power industry. It is also a must read for senior undergraduates, graduate students, and researchers involved in power system planning and operation.

Leseprobe

IEEE Press

445 Hoes Lane

Piscataway, NJ 08854

IEEE Press Editorial Board

Tariq Samad, Editor in Chief

Giancarlo Fortino

Xiaoou Li

Ray Perez

Dmitry Goldgof

Andreas Molisch

Linda Shafer

Don Heirman

Saeid Nahavandi

Mohammad Shahidehpour

Ekram Hossain

Jeffrey Nanzer

Zidong Wang

ADVANCES IN ELECTRIC POWER AND ENERGY SYSTEMS

Load and Price Forecasting

Edited by

Mohamed E. El-Hawary

Copyright © 2017 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Library of Congress Cataloging-in-Publication Data is available.

ISBN: 978-1-118-17134-9

Contents

Preface and Acknowledgments

Contributors

Chapter 1 Introduction

Prelude

Forecasting: General Considerations

Forecasting in Electric Power Systems

Load Forecasting in Electric Power Systems

Electricity Price Forecasting in Electric Power Systems

Time Series Analysis

Artificial Neural Networks

Overview of Chapters

References

Chapter 2 Univariate Methods for Short-Term Load Forecasting

Introduction

Intraday Load Data

Univariate Methods for Load Forecasting

Empirical Forecasting Study

Extensions of the Methods

Summary and Concluding Comments

Acknowledgments

References

Chapter 3 Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems

Introduction

Background

Weighted Nearest Neighbors Methodology

Performance assessment

Application to aggregated load forecasting

Application to pool energy price forecasting

Application to customer-level forecasting

Conclusions

References

Chapter 4 Electricity Prices as a Stochastic Process

Introduction

Characteristics of Electricity Prices

Stochastic Process Models for Electricity Prices

Time Series Methods for Electricity Prices

Numerical Examples

Conclusions

Acknowledgment

References

Chapter 5 Short-Term Forecasting of Electricity Prices Using Mixed Models

Introduction and Problem Statement

State of the Art

Time Series Analysis and Arima Models

Development of Mixed Models and Its Computational Implementation

Analysis of Forecasting Errors

Design of Experiments

Numerical Results

Conclusions

Acknowledgments

References

Chapter 6 Electricity Price Forecasting Using Neural Networks and Similar Days

Introduction

Solution Methodologies

Neural Network Approach for Price Prediction

Result Analysis and Discussion

Conclusions

Appendix

References

Chapter 7 Estimation of Post-Storm Restoration Times for Electric Power Distribution Systems

Introduction

Post-Storm Electric Power Outage Restoration Process

Restoration Modeling Approaches

Variable Definition and Data Sources

Accelerated Failure Time Outage Duration Models

Restoration Time Estimation

Conclusions and Future Work

Acknowledgments

References

Chapter 8 A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models

Introduction

Chaos Input Space Reconstruction

Automatic Clustering Algorithm

Bayesian Inference Applied to MLPs

Fully Automatic Bayesian Neural Forecaster

Results

Conclusion

References

Index

IEEE Press Series on Power Engineering

EULA

List of Table

Chapter 2

Table 2-1

Table 2-2

Table 2-3

Table 2-4

Table 2-5

Chapter 3

Table 3-1

Table 3-2

Table 3-3

Table 3-4

Table 3-5

Table 3-6

Table 3-7

Table 3-8

Table 3-9

Table 3-10

Table 3-11

Table 3-12

Table 3-13

Table 3-14

Table 3-15

Table 3-16

Table 3-17

Table 3-18

Table 3-19

Table 3-20

Table 3-21

Table 3-22

Table 3-23

Table 3-24

Table 3-25

Table 3-26

Chapter 4

Table 4-1

Table 4-2

Table 4-3

Table 4-4

Table 4-5

Table 4-6

Table 4-7

Chapter 5

Table 5-1

Table 5-2

Table 5-3

Table 5-4

Table 5-5

Table 5-6

Table 5-7

Table 5-8

Table 5-9

Table 5-10

Table 5-11

Table 5-12

Table 5-13

Table 5-14

Chapter 6

Table 6-1

Table 6-2

Table 6-3

Table 6-4

Table 6-5

Table 6-6

Table 6-7

Table 6-8

Table 6-9

Chapter 7

Table 7-1

Table 7-2

Table 7-3

Table 7-4

Table 7-5

Table 7-6

Table 7-7

Table 7-8

List of Illustrations

Chapter 1

Figure 1-1

Factors influencing electricity prices.

Figure 1-2

Typical backpropagation network.

Chapter 2

Figure 2-1

Electricity load (MW) in France, Norway, Portugal, and Spain from Sunday April 3, 2005 to Saturday October 29, 2005.

Figure 2-2

Electricity load (MW) in Finland, Great Britain, and Sweden from Sunday April 3, 2005 to Saturday October 29, 2005.

Figure 2-3

Electricity load (MW) in Belgium, Ireland, and Italy from Sunday April 3, 2005 to Saturday October 29, 2005.

Figure 2-4

Half-hourly electricity load (MW) in France and Great Britain from Sunday June 10, 2005 to Saturday June 23, 2005.

Figure 2-5

Half-hourly electricity load (MW) in Ireland and Portugal from Sunday June 10, 2005 to Saturday June 23, 2005.

Figure 2-6

For the in-sample period of the French series, lag 48 autocorrelation estimated separately for each period of the week.

Figure 2-7

Mean MAPE plotted against lead time for the 10 load series.

Figure 2-8

Mean relative MAE plotted against lead time for the 10 load series.

Figure 2-9

Mean MAPE for the 10 load series plotted against time of day for three-hour-ahead prediction.

Chapter 3

Figure 3-1

Illustration of the WNN approach.

Figure 3-2

False nearest neighbors

Figure 3-3

Histogram of load during 2004.

Figure 3-4

Evolution of load during October–December 2004.

Figure 3-5

Correlation coefficients.

Figure 3-6

Evolution of parameters

a

i

during 2005.

Figure 3-7

Hourly average of the prices time series (March 2001).

Figure 3-8

Histogram of prices during 2000 (top left), 2001 (top right), and 2002 (bottom).

Figure 3-9

Hourly price versus load during 2002.

Figure 3-10

Best daily prediction in a week of May 2002.

Figure 3-11

Worst daily prediction in a week of May 2002.

Figure 3-12

Initial available data.

Figure 3-13

Nearest days.

Figure 3-14

Restored curve.

Figure 3-15

Typical week from February 13–19, 2005. Teaching period.

Figure 3-16

Week from August 28 to September 3, 2005. Exam period begins.

Figure 3-17

Week from October 30 to November 5, 2005. Special holiday on November 1.

Figure 3-18

Day before the prediction day (current day) and its three nearest neighbors using the WNN method with LS coefficients and “W&W” classification.

Figure 3-19

Days after the three neighbors shown in Fig. 3-18.

Figure 3-20

Actual and predicted demands for October 5, 2005.

Figure 3-21

Actual demand and prediction. WNN method with LS coefficients and without any data base classification.

Figure 3-22

Relative errors for different data base classifications and IPD coefficients.

Figure 3-23

Actual and predicted demands for the 2-week period, July 3 to 16, 2005.

Figure 3-24

Week from October 30 to November 5, 2005 with a holiday on November 1.

Figure 3-25

Demands corresponding to November 1 and 8, Tuesdays.

Figure 3-26

Relative errors for WNN and AR methods. Regular week.

Figure 3-27

Relative prediction error of WNN and AR methods. Holiday on November 1.

Chapter 4

Figure 4-1

Prices of PJM spot market from April 2, 1998 to September 30, 2003.

Figure 4-2

Prices of England and Wales market from February 4, 2001 to March 3, 2004.

Figure 4-3

Prices of California market from April 1, 1998 to March 2, 2000.

Figure 4-4

Prices of Ontario market from May 1, 2002 to April 30, 2005.

Figure 4-5

Sample hourly electricity prices and load from August 1, 1998 to August 22, 1998, in California.

Figure 4-6

Nord pool market prices from January 1, 1997 to April 25, 2000.

Figure 4-7

Histogram for 2 months of PJM spot prices in summer 2000.

Figure 4-8

Sample paths of a Brownian motion.

Figure 4-9

Brownian motion and the range of uncertainty.

Figure 4-10

A sample path of Brownian motion with drift.

Figure 4-11

Several sample paths of GBM with constant mean and variance.

Figure 4-12

Variances of mean reversion process and Brownian motion with drift.

Figure 4-13

PJM electricity market from January 1, 1999 to March 31, 2002.

Figure 4-14

A structural model based on supply and demand.

Figure 4-15

PAR model.

Figure 4-16

The curves of actual price versus AR(4).

Figure 4-17

The curves of actual price versus ARX.

Chapter 5

Figure 5-1

Market clearing price, Spanish market.

Figure 5-2

Simulated series of stationary and non-stationary processes in the mean.

Figure 5-3

Stationary and nonstationary in variance processes.

Figure 5-4

Series A [17] and its sample ACF.

Figure 5-5

Simulated AR(1) processes and their ACF.

Figure 5-6

AR(1) processes and their PACF.

Figure 5-7

ACF and PACF of an MA(1) simulated process.

Figure 5-8

ACF and PACF of an ARMA(1,1) process.

Figure 5-9

Integrated process of order 1 and its first difference.

Figure 5-10

Random walk with drift and its ACF.

Figure 5-11

Hourly load and ACF and PACF, May 18–31, 2004, Spanish market.

Figure 5-12

Hourly loads depending on the day of the week, May 18–31, 2004, Spanish market.

Figure 5-13

Hourly time series of electricity prices, November 2004, Spanish market.

Figure 5-14

ACF and PACF, hourly prices in the 13th hour. Spanish market, 1998–2003.

Figure 5-15

Data and forecasts. Airline passenger data after taking logs and both regular and seasonal differences.

Figure 5-16

Boxplot of hourly prices (1998–2003).

Figure 5-17

Hourly prices (January 1, 1998–December 31, 2003).

Figure 5-18

Boxplot forecasting errors. Models 24, 48 (first and second column, respectively). Lengths 44, 80 weeks (first and second row, respectively).

Figure 5-19

Daily prediction errors. Model 48 for workdays and 24 for weekends. Length 44 weeks.

Figure 5-20

Smoothed prediction error for the whole period considered. Model 24 for weekends and Model 48 for weekdays. Length equal to 44 weeks in both cases. First forecast computed for the 45th week in 1998 since the first 44 weeks are used to identify and estimate the model.

Figure 5-21

Structure of the data corresponding to an experiment with two factors and replications.

Figure 5-22

Structure of the data corresponding to an experiment with three factors.

Figure 5-23

Main effect Model (weekends). Means and 95% Bonferroni intervals.

Figure 5-24

Main effect Length (weekends). Means and 95% Bonferroni intervals.

Figure 5-25

Main effect Model (weekdays). Means and 95% Bonferroni intervals.

Figure 5-26

Main effect of Length (weekdays). Means and 95% Bonferroni intervals.

Figure 5-27

Forecasts and real prices (May 25–31, 2000).

Figure 5-28

Forecasts and real prices (August 25–31, 2000).

Figure 5-29

Forecasts and real prices, February 18–24, 2002.

Figure 5-30

Forecasts and real prices, May 20–26, 2002.

Figure 5-31

Forecasts and real prices, August 19–25, 2002.

Figure 5-32

Forecasts and real prices, November 18–24, 2002.

Figure 5-33

Forecasts and real prices (December 15–21, 2001).

Figure 5-34

Real prices and forecasts April 15–21, 2002.

Figure 5-35

Boxplot of prediction errors for the whole period considered.

Chapter 6

Figure 6-1

Relationship between LMP and load in the PJM (January–May, 2006).

Figure 6-2

Time framework for the selection of similar price days corresponding to forecast day (ANN approach).

Figure 6-3

Proposed ANN model for day-ahead price forecasting.

Figure 6-4

Recursive neural network model for price forecasting.

Figure 6-5

Flowchart for developed methodology to forecast prices.

Figure 6-6

Actual and forecast day-ahead PJM electricity price (Friday, January 20, 2006).

Figure 6-7

Actual and forecast day-ahead PJM electricity prices (Friday, February 10, 2006).

Figure 6-8

Actual and forecast day-ahead PJM electricity price (Sunday, March 5, 2006).

Figure 6-9

Actual and forecast day-ahead PJM electricity price (Friday, April 7, 2006).

Figure 6-10

Actual and forecast day-ahead PJM electricity price (Saturday, May 13, 2006).

Figure 6-11

Actual L MP and load: January−May, 2006 in the PJM market.

Figure 6-12

Weekly price forecasts during low demand week (Wednesday, February 1 to Tuesday, February 7, 2006).

Figure 6-13

Weekly price forecasts during high demand week (Wednesday, February 22 to Tuesday, February 28, 2006).

Figure 6-14

Relationship between LMP and load in day-ahead PJM market (January−December, 2006).

Figure 6-15

Time framework for the selection of similar days corresponding to forecast day (RNN approach).

Figure 6-16

Actual and forecasted LMPs for PJM market in winter (Monday, December 4, 2006).

Figure 6-17

Actual and forecasted LMPs for PJM market in spring (Saturday, May 13, 2006).

Figure 6-18

Actual and forecasted LMPs for PJM market in summer (Monday, July 10, 2006).

Figure 6-19

Actual and forecasted LMPs for PJM market in autumn (Thursday, November 2, 2006).

Figure 6-20

Actual and forecasted LMPs for PJM market in spring (Friday, April 28 to Sunday, April 30, 2006).

Figure 6-21

Actual and forecasted LMPs for PJM market in summer (Thursday, June 8 to Saturday, June 10, 2006).

Chapter 7

Figure 7-1

Dominion, Duke, and Progress service areas with recent hurricane tracks.

Figure 7-2

Function of survival function estimate versus ln(

t

) for (a) Weibull, (b) log-logistic, and (c) log-normal AFT models.

Figure 7-3

Kaplan–Meier estimate of cumulative hazard versus Weibull AFT Cox–Snell residuals for the (a) hurricane and (b) ice storm model.

Figure 7-4

(a) Scaled score residuals versus covariate

maximum gust wind speed

(

x

w

) and (b) deviance residuals versus covariate

number of customers

(

x

cus

) for the final recommended AFT hurricane duration model.

Figure 7-5

Histogram of final AFT hurricane outage duration model raw residuals (observed–predicted) in hours for the testing set (Hurricane Charley).

Figure 7-6

AFT hurricane outage duration model raw residuals (observed–predicted) in hours versus

number of customers affected by outage

(

x

cus

) for the testing set (Hurricane Charley).

Figure 7-7

Histogram of final AFT ice storm outage duration model raw residuals (observed–predicted) in hours for the testing set (January 2004 ice storm).

Figure 7-8

AFT ice storm outage duration model raw residuals (observed–predicted) in hours versus

number of customers affected by outage

(

x

cus

) for the testing set (January 2004 ice storm).

Figure 7-9

Flowchart of restoration time estimation method.

Figure 7-10

Restoration curves for (a) Hurricane Charley and (b) the January 2004 ice storm. Actual, predicted with observed covariates, and predicted with simulated covariates, median curves with 90% confidence intervals.

Figure 7-11

Actual time (hours) to restore 90% of customers versus median of time to restore 90% of customers from 20,000 simulated samples for (a) Hurricane Charley and (b) the January 2004 ice storm based on recommended AFT models using

observed

covariate values for

x

cus

,

x

dev

, and

x

start

(each point represents one county in the Progress Energy service area).

Figure 7-12

Actual time (hours) to restore 90% of customers versus median of time to restore 90% of customers from 20,000 simulated samples for (a) Hurricane Charley and (b) the January 2004 ice storm based on recommended AFT models using

simulated

covariate values for

x

cus

,

x

dev

and

x

start

(each point represents one county in the Progress Energy service area)

Chapter 8

Figure 8-1

Grid coordinates of the Rio Grande basin, which has 122 rainfall monitoring stations.

Figure 8-2

ANNs' training adaptation and testing.

Guide

Cover

Table of Contents

Preface and Acknowledgments

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I

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V

Preface and Acknowledgments

My goal in writing this book is to present a sampling of leading-edge works treating forecasting problems in the operation of electric power systems throughout the world.

The book's audience consists mainly of practicing professionals, regulators, planners, and consultants engaged in the electric power business. In addition, senior undergraduate and graduate students and researchers will find this book to be useful in their work. Background requirements include some mathematical notions from algebra and calculus.

The core of this book (Chapters 2–6) consists of two chapters dealing with power system load forecasting and three chapters on electricity price forecasting. Chapters 7 and 8 are unique treatments of estimation of post-storm restoration times in electric power distribution systems and river flow forecasting based on autonomous neural network models using a nonparametric approach. While Chapter 1 is usually devoted to charting the course of the book, I prepared Chapter 1 to offer background material for the two main forecasting issues considered. Each chapter is a self-contained treatment of its subject matter.

I am indebted to our Editor, Ms. Mary Hatcher. Her patience and constant encouragement contributed to the evolution of this book. My family is the source of the continuing motivation to complete this manuscript.

MOHAMED E. EL-HAWARY

Halifax, Nova Scotia, Canada

Contributors

Alexandre P. Alves da Silva is with GE Global Research, Brazil Technology Center, Rio de Janeiro, Brazil. He received his PhD degree in Electrical Engineering from the University of Waterloo, Canada. During 1999, he was a Visiting Professor in the Department of Electrical Engineering, University of Washington, Seattle, WA. He has authored and co-authored more than 200 papers on intelligent systems application to power systems. Professor Alves da Silva was the TPC Chairman of the First Brazilian Conference on Neural Networks in 1994 and of the International Conference on Intelligent System Applications to Power Systems (ISAP) in 1999, the first time it was held in Brazil. He is an IEEE fellow.

Tatiyana V. Apanasovich is an Associate Professor of Statistics at George Washington University, Washington, DC. Her research concerns measurement error models, non/semiparametric regression, and spatial statistics.

Rachel A. Davidson is a Professor in the Department of Civil and Environmental Engineering and a core faculty member in the Disaster Research Center at the University of Delaware. She received a PhD from Stanford University. Davidson conducts research on natural disaster risk modeling and civil infrastructure systems. Her work involves developing new engineering models to better characterize the impact of future natural disasters, and using that understanding to support decisions to help reduce future losses. She is a fellow and past president of the Society for Risk Analysis.

Vitor Hugo Ferreira is currently with Universidade Federal Fluminense (UFF), Niterói, Brazil. He received his BSc degree in Electrical Engineering from the Federal University of Itajubá in 2002, and MSc and DSc degrees in Electrical Engineering from the Federal University of Rio de Janeiro (COPPE/UFRJ) in 2005 and 2008, respectively, all in Brazil. His research interests include time series forecasting and neural networks. He is the Chairman of Electrical Engineering Department at UFF.

Carolina García-Martos is an Associate Professor at the Escuela Tecnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid (UPM), Spain. She received her degree in Industrial Engineering and her PhD in Applied Statistics from the Universidad Politécnica de Madrid in July 2005 and June 2010, respectively. She has received several awards for her PhD thesis: Extraordinary Prize from the UPM, the Loyola de Palacio Best PhD Prize on EU Energy Policy given by the European University Institute (EUI), Florence School of Regulation and Loyola de Palacio Chair (Third Prize Winner), the “ELECNOR” PhD Thesis Award and the PhD Thesis Special Mention by the Professional Association of Industrial Engineers of Madrid (Spain). She has published her works in Technometrics, IEEE Transactions on Power Systems, Applied Energy, Energy Economics, and Wiley Encyclopedia of Electrical and Electronics Engineering, among others.

Antonio Gómez-Expósito received an Industrial Engineering degree, major in electrical engineering, and a Doctor of Engineering degree in Power Engineering from the University of Seville, Spain, in 1982 and 1985, respectively. He is currently the Endesa Red Industrial Chair Professor with the University of Seville. His research interests include optimal power system operation, state estimation, digital signal processing, and control of flexible ac transmission system devices.

Catalina Gómez-Quiles received an engineering degree from the University of Seville, Spain, and an MSc degree from McGill University, Montreal, Canada, both in Electrical Engineering. In 2012, she got a PhD degree from the University of Seville. Her research interests relate to mathematical and computer models for power system analysis.

Yunhe Hou received his BE and PhD degrees in Electrical Engineering from the Huazhong University of Science and Technology, Wuhan, China, in 1999 and 2005, respectively. He was a post-doctoral research fellow at Tsinghua University, Beijing, China, and a post-doctoral researcher at Iowa State University, Ames, IA, and the University College Dublin, Dublin, Ireland. He was also a Visiting Scientist at the Massachusetts Institute of Technology, Cambridge, MA. He is currently an Associate Professor with the Department of Electrical and Electronic Engineering at the University of Hong Kong, Hong Kong.

Chen-Ching Liu received his PhD from the University of California, Berkeley, CA. He is Boeing Distinguished Professor at Washington State University, Pullman, WA, and Visiting Professor at University College Dublin, Ireland. He was a Palmer Chair Professor at Iowa State University and a Professor at the University of Washington, Seattle, WA. Dr. Liu received the IEEE PES Outstanding Power Engineering Educator Award in 2004. He was recognized with a Doctor Honoris Causa from University Politehnica of Bucharest, Romania. Professor Liu is a fellow of the IEEE and member of the Washington State Academy of Sciences.

Haibin Liu is with State Key Laboratory of Power Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, China.

Paras Mandal is an Assistant Professor of Electrical and Computer Engineering and Director of Power and Renewable Energy Systems (PRES) Lab at the University of Texas at El Paso (UTEP). He received his ME and BE degrees from Thailand and India, respectively, and a PhD degree from the University of the Ryukyus, Japan. His research interests include AI application to forecasting problems, renewable energy systems, power systems operations and markets, power system optimization, and smart grid.

José L. Martínez-Ramos received a PhD degree in Electrical Engineering from the University of Sevilla, Spain. Since 1990, he has been with the Department of Electrical Engineering, University of Sevilla, where he is currently a Professor. His primary areas of interest are active and reactive power optimization and control and power system analysis.

Patrick E. McSharry is a senior research fellow at the Smith School of Enterprise and the Environment, faculty member of the Oxford Man Institute of Quantitative Finance at Oxford University, Visiting Professor at the Department of Electrical and Computer Engineering, Carnegie Mellon University, and will lead the new World Bank-funded African Centre of Excellence in Data Science (ACE-DS) based in Rwanda. He is a fellow of the Royal Statistical Society, a senior member of the IEEE, and a senior academic member of the Willis Research Network. He takes a multidisciplinary approach to developing quantitative techniques for data science, decision-making, and risk management. His research focuses on Big Data, forecasting, predictive analytics, machine learning, and the analysis of human behavior. He has published over 100 peer-reviewed papers, participated in knowledge exchange programs, and consults for national and international government agencies and the insurance, finance, energy, telecoms, environment, and healthcare sectors. Patrick received a first-class honours BA in Theoretical Physics and an MSc in Engineering from Trinity College Dublin and a DPhil in Mathematics from Oxford University.

Michael Negnevitsky is a Professor/Chair in Power Engineering and Computational Intelligence and the Director of the Centre for Renewable Energy and Power Systems (CREPS) at the University of Tasmania, Hobart, Australia. He received a PhD degree from Byelorussian University of Technology, Minsk, Belarus. He was a senior research fellow and a Senior Lecturer in the Department of Electrical Engineering, Byelorussian University of Technology, Minsk.

Jesús M. Riquelme-Santos received his PhD degree in Electrical Engineering from the University of Sevilla, Spain. Since 1994, he has been with the Department of Electrical Engineering, University of Seville, where he is currently an Associate Professor. His areas of interest are active and reactive power optimization and control, power system analysis, and power quality and forecasting techniques.

José C. Riquelme received a PhD degree in Computer Science from the University of Sevilla, Spain. Since 1987, he has been with the Department of Computer Science, University of Sevilla, where he is currently an Associate Professor. His primary areas of interest are data mining, knowledge discovery in databases, and machine learning techniques.

Julio Rodríguez has a BA in Mathematics from the Universidad Autónoma de Madrid and a PhD in Mathematics from the Universidad Carlos III de Madrid. He was a Visiting Associate Professor at the Graduate School of Business (GSB) at the University of Chicago in 2002, and an Associate Professor of Statistics at the Universidad Politecnica de Madrid, from 2003 to 2005. Since 2006, he is an Associate Professor of Econometrics and Statistical Methods at the Departamento de Análisis Económico: Economía Cuantitativa, Universidad Autónoma de Madrid. His areas of interest are applications in electricity markets, time series, multivariate analysis, graphical methods, and functional data. He has published his work in Journal of the American Statistical Association, Technometrics, International Journal of Forecasting, and Journal of Multivariate Analysis, among others.

Harold Salazar received a PhD degree in Electrical Engineering and an MS degree in Economics from Iowa State University, Ames, IA. He is currently a Professor at the Technological University of Pereira (Universidad Tecnológica de Pereira), Colombia. He is also a consultant for the National Energy and Gas Regulatory Commission of Colombia (CREG in Spanish) and for the Power Market Operator of Colombia.

María Jesús Sánchez is an Associate Professor of Statistics at the Escuela Técnica Superior de Ingenieros Industriales Universidad Politécnica de Madrid. She obtained her degree in Electrical Engineering and her PhD in Applied Statistics both from the Universidad Politécnica de Madrid. Her research areas of interest are outliers in time series, Kriging models, reliability of electric power generating systems, load and prices forecasting models, and dimensionality reduction techniques with application to liberalized electricity markets forecasting. She has published her works in Technometrics, Computational Statistics and Data Analysis, Reliability Engineering and System Safety, and IEEE Transactions on Power Systems, among others.

Tomonobu Senjyu is a Professor with the Department of Electrical and Electronics Engineering, University of the Ryukyus. He received her BS and MS degrees in Electrical Engineering from the University of the Ryukyus, Okinawa, and a PhD degree in Electrical Engineering from Nagoya University, Nagoya, Japan. His research interests are in the areas of power system optimization and operation, electricity market, intelligent systems, power electronics, renewable energy, and smart grid.

Anurag K. Srivastava is an Associate Professor in the School of Electrical Engineering and Computer Science at Washington State University, Pullman, WA. In 2005, he received his PhD degree from Illinois Institute of Technology (IIT), Chicago, IL.

James W. Taylor is a Professor of Decision Science at the Saïd Business School of the University of Oxford. His research is in the area of time series forecasting, and he teaches analytics courses for the Oxford MBA and Executive MBA Programmes. He has a PhD in Time Series Forecasting from the London Business School. He is a former Associate Editor of the International Journal of Forecasting and Management Science.

Alicia Troncoso is a Professor in the School of Engineering at Pablo de Olavide University in Seville, Spain. She received a PhD degree in Computer Science from the University of Seville, Spain, in 2005. Presently, she is an Associate Professor at the University Pablo de Olavide, Seville. Her primary areas of interest are time series analysis, control and forecasting, and optimization techniques.