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What has been the most significant advancement in recent years in this field? And what are the biggest challenges ahead?

With smart-meter 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 short-term forecasts by focusing on peaks. For example, it is a first time that permutation-based distances and forecasts that allow small time-shifts between electric profiles are reviewed in a book. The application of extreme value theory to improve on time-series 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 well-separated and self-contained 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.

  • Jacob, M., Neves, C.and Vukadinovic-Greetham, D. (2020). Forecasting and Assessing Risk of Individual Electricity Peaks. Springer Briefs in Mathematics of Planet Earth.  ISBN 978-3-030-28668-2. Open Access. Hardcopy.

  • Gomes, M.I., Fraga Alves, M.I. and Neves, C. (2013). Análise de Valores Extremos: Uma Introdução. INE - National Statistics Institute, Portugal. ISBN: 978-972-8890-30-8. (In Portuguese)

  • Hall, A., Neves, C. and Pereira, A. (2011). Grande Maratona Estatística no SPSS. Escolar Editora. ISBN: 978-972-5923-01-6. (In Portuguese)


  • Israelsson, J., Black, E., Neves, C. and Walshaw, D. (2024+). Estimation and reduced bias estimation of the residual dependence index with unnamed marginals. Working paper. Supporting information available here.

  • Deb, S., Neves, C. and Roy, S. (2024). Nonparametric quantile estimation for spatio-temporal processes. Submitted.

Between 2022 and 2023, I have developed impact-focused research work in collaboration with EDF R&D UK Centre.

  • Einmahl, J.H.J., Ferreira, A., de Haan, L. Neves, C. and Zhou, C. (2022). Spatial dependence and space-time trend in extreme events. Annals of Statistics, 50(1), 30-52. Supplement available at:

  • Ficchì, A., Cloke, H., Neves, C., Woolnough, S., Coughlan de Perez, E., Zsoter, E., Pinto, I., Meque. A. and Stephens, E. (2021). Beyond El Niño: Unsung climate modes drive African floods. Weather and Climate Extremes, 33, 100345. Open Access through:

  • Konzen, E., Neves, C. and Jonathan, P. (2021). Modelling non-stationary extremes of storm severity: comparing parametric and semi-parametric inference. Environmetrics, 32(4), pp. e2667, Open Access, Statsview.

  • Israelsson, J., Black, E., Neves, C., Torgbor, F.F., Tanu, M., and Lamptey, P.N.L. (2020). The spatial correlation structure of rainfall at the local scale over southern Ghana. Journal of Hydrology: Regional Studies, 31, 100720. OA through:

  • Ferreira, A., Friederichs, P., de Haan, L., Neves, C. and Schlather, M. (2017). Estimating space-time 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, 199-237

  • de Haan, L., Klein Tank, A. and Neves, C. (2015). On tail trend detection: modeling relative risk, Extremes, 18, 141-178

  • Henriques-Rodrigues, L., Gomes, M.I., Fraga Alves, M.I. and Neves, C. (2014). PORT-estimation of a shape second order parameter, REVSTAT, 12, 299-328

  • Fraga Alves, I. and Neves, C. (2014). Estimation of the finite right endpoint in the Gumbel domain, Statistica Sinica, 24, 1811-1835.

  • 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, 342-355

  • Neves, C. and Pereira, A. (2010). Detecting finiteness in the right endpoint of light tailed distributions, Statistics & Probability Letters, 80, 437-444

  • Neves, C. (2009). From extended regular variation to regular variation with application in extreme value statistics, Journal of Mathematical Analysis and Applications, 355, 216-230

  • Fraga Alves, M.I., Gomes, M.I., de Haan, L. and Neves, C. (2009). Mixed moment estimator and location invariant alternatives, Extremes, 12, 149-185

  • 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, 213-227

  • de Haan, L., Neves, C. and Peng, L. (2008). Parametric tail copula estimation and model testing, Journal of Multivariate Analysis, 99, 1260-1275

  • 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, 83-100

  • Gomes, M.I. and Neves, C. (2008). Asymptotic comparison of the mixed moment and classical extreme value index estimators. Statistics & Probability Letters, 78, 643-653

  • 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, 285-304

  • Neves, C. and Fraga Alves, M.I. (2007). Semi-parametric approach to the Hasofer-Wang and Greenwood statistics in Extremes. Test, 16, 297-313

  • Neves, C., Picek, J. and Fraga Alves, M.I. (2006). The contribution of the maximum to the sum of excesses for testing max-domains of attraction. Journal of Statistical Planning and Inference, 136, 1281-1301

  • 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, 689-704

  • Fraga Alves, I. and Neves, C. (2016). Extreme Value Theory: an introductory overview. Extreme Events in Finance, 53-95. doi:10.1002/9781118650318.ch4 

  • 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 Bar-Hen, A. (Eds.), p. 185-195. Springer. Selected Papers of 45th Meeting of the Italian Statistical Society (SIS2010) Padua, Italy, 2010. ISBN 978-3642355882

  • Fraga Alves, I., Neves, C. and Corman, U. (2011). Heavy and Super-heavy Tail Analysis. In: Laws of Small Numbers: Extremes and Rare Events. Falk, M., Huesler, J. and Reiss, R.-D. (Eds.), p. 75-101. Birkhauser, Springer Basel. ISBN: 978-30348000823-01-6

  • Fraga Alves, I. and Neves, C. (2011). Extreme Value Distributions. In: International Encyclopedia of Statistical Science. Lovric, Miodrag (Eds.), p. 493-496. Springer. ISBN: 978-3-642-04897-5

  • 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. 181-194, Birkhauser, Boston. ISBN: 978-0817646257

Why the forecasting of electricity peaks is a highly important topic?

In an era of inter-connectivity 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.

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