Publications
What has been the most significant advancement in recent years in this field? And what are the biggest challenges ahead?
With smartmeter rollout, renewables and low carbon technologies, such as electric vehicles and heat pumps, individual level energy forecasts are becoming increasingly important. Furthermore, across different areas of data science applications, there is a recognition that hybrid methodologies which combine machine learning and statistical methodology can improve computation times and increase predictive power from pooling highly complex data generated by different sources. For example, combining traditional approaches to forecasting with databases encompassing social and human behaviour indicators. Hence, the challenge is responsible innovation in terms of striking the right balance between protecting privacy and data sharing in the development of future energy management approaches.
How can the research in this field help in the context of grand societal challenges?
In order to be able to manage our electric energy consumption and have a flexible, efficient and reliable energy management system we need good forecasts. This is necessary ingredient for all kinds of applications: storage control, system modelling, making business cases for low carbon technologies uptake, running different scenarios for policy development, etc. Our research is a step in this direction.
What sets this book apart from other books?
This book is the culmination of a research project with a major player in the energy sector. It combines mathematical fundamentals, machine learning techniques and specific statistical methodology for extreme values in order to address problems of practical importance in shortterm forecasts by focusing on peaks. For example, it is a first time that permutationbased distances and forecasts that allow small timeshifts between electric profiles are reviewed in a book. The application of extreme value theory to improve on timeseries modelling of electricity load profiles is carried out in a truly novel setting, which makes this book one of a kind.
Why did you choose to publish this book as an open access book and what do you envisage from this publication?
We believe that electric energy efficiency is extremely important for carbon footprint reduction, so we are glad that the knowledge can be openly shared, including code and data we use in the book. We have designed and structured the book in such a way that wellseparated and selfcontained chapters (although with possible interconnection moderated by the particular application to peak electricity demands) can be used for teaching in class and/or in empowering students to promote independent learning.
Why the forecasting of electricity peaks is
a highly important topic?
In an era of interconnectivity of modern society, information collection and intensive high performance computation, mass digital disruption is one of the worst scenarios that could face the energy sector. Technology has become ubiquitous and wherever you position yourself on the sliding scale from a centrally controlled energy market, through control distributed generation, towards consumer participation in energy storage (batteries), accurate and timely forecasts for peak demands are a fundamental part of any efficient energy management plan in order to avoid power shortages.
BOOKS

Jacob, M., Neves, C.and VukadinovicGreetham, D. (2020). Forecasting and Assessing Risk of Individual Electricity Peaks. Springer Briefs in Mathematics of Planet Earth. ISBN 9783030286682. Open Access.

Gomes, M.I., Fraga Alves, M.I. and Neves, C. (2013). Análise de Valores Extremos: Uma Introdução. INE  National Statistics Institute, Portugal. ISBN: 9789728890308. (In Portuguese)

Hall, A., Neves, C. and Pereira, A. (2011). Grande Maratona Estatística no SPSS. Escolar Editora. ISBN: 9789725923016. (In Portuguese)
PAPERS

Einmahl, J.H.J., Ferreira, A., de Haan, L., Neves, C. and Zhou, C. (2020). Spatial dependence and spacetime trend in extremes. ArXiv:2003.04265. Supplement available at: https://bit.ly/3aJFM6B

Ferreira, A., Friederichs, P., de Haan, L., Neves, C. and Schlather, M. (2017). Estimating spacetime trend and dependence of heavy rainfall. ArXiv:1707.04434

Fraga Alves, I., Neves, C. and Rosário, P. (2017). A general estimator for the right endpoint with an application to supercentenarian women’s records. Extremes, 20, 199237

HenriquesRodrigues, L., Gomes, M.I., Fraga Alves, M.I. and Neves, C. (2014). PORTestimation of a shape second order parameter, REVSTAT, 12, 299328

de Haan, L., Klein Tank, A. and Neves, C. (2015). On tail trend detection: modeling relative risk, Extremes, 18, 141178

Fraga Alves, I. and Neves, C. (2014). Estimation of the finite right endpoint in the Gumbel domain, Statistica Sinica, 24, 18111835

Neves, C. (2013). Book review: Categorical Data Analysis, third edition, by Alan Agresti. CJAS Journal of Applied Statistics, doi:10.1080/02664763.2013.854979

Neves, C., Gomes, M.I. and Fraga Alves, M.I. (2011). Extreme nitriding limits in aluminum extrusion, International Journal of Mathematical Modelling and Numerical Optimisation – Special Issue on “Mathematics and Industry”, 2, 342355

Neves, C. and Pereira, A. (2010). Detecting finiteness in the right endpoint of light tailed distributions, Statistics & Probability Letters, 80, 437444

Neves, C. (2009). From extended regular variation to regular variation with application in extreme value statistics, Journal of Mathematical Analysis and Applications, 355, 216230

Fraga Alves, M.I., Gomes, M.I., de Haan, L. and Neves, C. (2009). Mixed moment estimator and location invariant alternatives, Extremes, 12, 149185

Fraga Alves, I., de Haan, L. and Neves, C. (2009). A test procedure for detecting super heavy tails, Journal of Statistical Planning and Inference, 139, 213227

de Haan, L., Neves, C. and Peng, L. (2008). Parametric tail copula estimation and model testing, Journal of Multivariate Analysis, 99, 12601275

Neves, C. and Fraga Alves, I. (2008). Testing extreme value conditions – an overview and recent approaches. REVSTAT Special issue on “Statistics of Extremes and Related Fields”, 6, 83100

Gomes, M.I. and Neves, C. (2008). Asymptotic comparison of the mixed moment and classical extreme value index estimators. Statistics & Probability Letters, 78, 643653

Fraga Alves, M.I., Gomes, M.I., de Haan, L. and Neves, C. (2007). A note on second order conditions in Extreme Value Theory: linking general and heavy tails conditions. REVSTAT, 5, 285304

Neves, C. and Fraga Alves, M.I. (2007). Semiparametric approach to the HasoferWang and Greenwood statistics in Extremes. Test, 16, 297313

Neves, C., Picek, J. and Fraga Alves, M.I. (2006). The contribution of the maximum to the sum of excesses for testing maxdomains of attraction. Journal of Statistical Planning and Inference, 136, 12811301

Neves, C. and Fraga Alves, M.I. (2004). Reiss and Thomas’ automatic selection of the number of extremes. Journal of Computational Statistics and Data Analysis, 47, 689704
BOOK CHAPTERS

Fraga Alves, I. and Neves, C. (2016). Extreme Value Theory: an introductory overview. In:Extreme Value Theory and Applications in Finance, Handbook Series in Financial Engineering and Econometrics, Wiley

Fraga Alves, I., de Haan, L. and Neves, C. (2013). How far can Man go? In: Advances in Theoretical and Applied Statistics. Torelli, N., Pesarin, F., and BarHen, A. (Eds.), p. 185195. Springer. Selected Papers of 45th Meeting of the Italian Statistical Society (SIS2010) Padua, Italy, 2010. ISBN 9783642355882

Fraga Alves, I., Neves, C. and Corman, U. (2011). Heavy and Superheavy Tail Analysis. In: Laws of Small Numbers: Extremes and Rare Events. Falk, M., Huesler, J. and Reiss, R.D. (Eds.), p. 75101. Birkhauser, Springer Basel. ISBN: 97830348000823016

Fraga Alves, I. and Neves, C. (2011). Extreme Value Distributions. In: International Encyclopedia of Statistical Science. Lovric, Miodrag (Eds.), p. 493496. Springer. ISBN: 9783642048975

Neves, C. and Fraga Alves, I. (2008). The Ratio of Maximum to the Sum for Testing Super Heavy Tails. In: Advances in Mathematical and Statistical Modeling. Arnold, B.C., Balakrishnan, N., Sarabia, J.M., Mínguez, R. (Eds.), p. 181194, Birkhauser, Boston. ISBN: 9780817646257