Faster model fitting for quantile additive models

b198050c-2e2a-4106-bcda-9c10944ab559

Active

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

No funds listed.

Sept. 30, 2021

Sept. 19, 2025

Reliable electricity load forecasts are an essential input for electricity production planning and power grid management. The UK has historically relied heavily on fossil fuel power plants, whose high ramp-up rates made adjusting for forecasting errors easy. As such stations are replaced with less flexible nuclear plants and renewables, the network will become much more reliant on accurate forecasts. The need to reduce carbons emission is driving the growth of electric vehicles sales, in addition to the transition to renewable production technologies (e.g., solar panels and wind turbines). Future grid management systems will coordinate distributed production and storage resources to manage, in a cost-effective fashion, the increased load and variability brought by the electrification of transportation and by a higher share of weather-dependent production. Electricity demand forecasts at a low level of aggregation, possibly down to the individual household, will be key inputs for such systems.

The project will focus on developing new non-parametric regression methods aimed at tackling upcoming forecasting challenges in the electricity industry. The particular model class which will be considered is that of generalized additive models (GAMs), which are widely-used in energy applications because they provide an appealing balance between flexibility, interpretability and scalability to Big Data. The project will build particularly on Fasiolo et al. (2020), which proposed new methods for distribution-free quantile GAMs (QGAMs). QGAMs often outperform standard GAMs in terms of accuracy in probabilistic electricity demand forecasting, due to the lack of any parametric assumption on the distribution of the response variable. However, QGAMs are computationally slower than GAMs, which limits the utility of such methods for larger data sets (e.g., smart meter data). The project will focus on developing new statistical methods for accelerating QGAM model fitting. Doing so will require theoretical/methodological work aimed at developing the underlying statistical methodology as well as computational work involving numerical linear algebra methods. It is potentially high impact because faster fitting methods will dramatically increase the usefulness of QGAMs for electricity demand forecasting on large data set. Further, the new methods should apply to loss-based models in general and could be used in many other application areas of (Q)GAMs, such as ecology, epidemiology, precision agriculture and business analytics to name a few.

Potential Impact:
The COMPASS Centre for Doctoral Training will have the following impact.

Doctoral Students Impact.

I1. Recruit and train over 55 students and provide them with a broad and comprehensive education in contemporary Computational Statistics & Data Science, leading to the award of a PhD. The training environment will be built around a set of multilevel cohorts: a variety of group sizes, within and across year cohort activities, within and across disciplinary boundaries with internal and external partners, where statistics and computation are the common focus, but remaining sensitive to disciplinary needs. Our novel doctoral training environment will powerfully impact on students, opening their eyes to not only a range of modern technical benefits and opportunities, but on the power of team-working with people from a range of backgrounds to solve the most important problems of the day. They will learn to apply their skills to achieve impact by collaborative working with internal and external partners, such as via our Rapid Response Teams, Policy Workshops & Statistical Clinics.

I2. As well as advanced training in computational statistics and data science, our students will be impacted by exposure to, and training in, important cognate topics such as ethics, responsible innovation, equality, diversity and inclusion, policy, effective communication and dissemination, enterprise, impact and consultancy skills. It is vital for our students to understand that their training will enable them to have a powerful impact on the wider world, so, e.g., AI algorithms they develop should not be discriminatory, and statistical methodologies should be reproducible, and statistical results accurately and comprehensibly communicated to the general public and policymakers.

I3. The students will gain experience via collaborations with academic partners within the University in cognate disciplines, and a wide range of external industrial & government partners. The students will be impacted by the structured training programmes of the UK Academy of Postgraduate Training in Statistics, the Bristol Doctoral College, the Jean Golding Institute, the Alan Turing Institute and the Heilbronn Institute for Mathematical Sciences, which will be integrated into our programme.

I4. Having received an excellent training, the students will then impact powerfully on the world in their future fruitful careers, spreading excellence.

Impact on our Partners & ourselves.

I5. Direct impacts will be achieved by students engaging with, and working on projects with, our academic partners, with discipline-specific problems arising in engineering, education, medicine, economics, earth sciences, life sciences and geographical sciences, and our external partners Adarga, the Atomic Weapons Establishment, CheckRisk, EDF, GCHQ, GSK, the Office for National Statistics, Sciex, Shell UK, Trainline and the UK Space Agency. The students will demonstrate a wide range of innovation with these partners, will attract engagement from new partners, and often provide attractive future employment matches for students and partners alike.

Wider Societal Impact

I6. COMPASS will greatly benefit the UK by providing over 55 highly trained PhD graduates in an area that is known to be suffering from extreme, well-known, shortages in the people pipeline nationally. COMPASS CDT graduates will be equipped for jobs in sectors of high economic value and national priority, including data science, analytics, pharmaceuticals, security, energy, communications, government, and indeed all research labs that deal with data. Through their training, they will enable these organisations to make well-informed and statistically principled decisions that will allow them to maximise their international competitiveness and contribution to societal well-being. COMPASS will also impact positively on the wider student community, both now and sustainably into the future.