# Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain

arXiv: https://arxiv.org/abs/2211.07451 · HTML: https://mlsanity.com/paper/2211.07451/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [Smoothing parameter and model selection for general smooth models](https://mlsanity.com/paper/1511.03864/) (2015)
- [Stability selection for component-wise gradient boosting in multiple dimensions](https://mlsanity.com/paper/1611.10171/) (2016)
- [Multivariate Conditional Transformation Models](https://mlsanity.com/paper/1906.03151/) (2019)
- [Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions](https://mlsanity.com/paper/1905.04022/) (2019)
- [Cholesky-based multivariate Gaussian regression](https://mlsanity.com/paper/2102.13518/) (2021)
- [Deselection of Base-Learners for Statistical Boosting -- with an Application to Distributional Regression](https://mlsanity.com/paper/2202.01657/) (2022)
- [Generalized Additive Models for Pair-Copula Constructions](https://mlsanity.com/paper/1608.01593/) (2016)
- [Additive stacking for disaggregate electricity demand forecasting](https://mlsanity.com/paper/2005.10092/) (2020)
- [A generalized Fellner-Schall method for smoothing parameter estimation with application to Tweedie location, scale and shape models](https://mlsanity.com/paper/1606.04802/) (2016)
- [Multivariate Covariance Generalized Linear Models](https://mlsanity.com/paper/1504.01551/) (2015)
- [Boosting Distributional Copula Regression](https://mlsanity.com/paper/2202.12851/) (2022)
- [Boosting Multivariate Structured Additive Distributional Regression Models](https://mlsanity.com/paper/2207.08470/) (2022)

## Built on by (most cited first, 1 of 1)

- [Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2504.02518/) (2025)

## Abstract (arXiv)

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here, we forecast the joint distribution of net-demand across the 14 regions constituting Great Britain's electricity network. Joint modelling is complicated by the fact that the net-demand variability within each region, and the dependencies between regions, vary with temporal, socio-economical and weather-related factors. We accommodate for these characteristics by proposing a multivariate Gaussian model based on a modified Cholesky parametrisation, which allows us to model each unconstrained parameter via an additive model. Given that the number of model parameters and covariates is large, we adopt a semi-automated approach to model selection, based on gradient boosting. In addition to comparing the forecasting performance of several versions of the proposed model with that of two non-Gaussian copula-based models, we visually explore the model output to interpret how the covariates affect net-demand variability and dependencies. The code for reproducing the results in this paper is available at https://doi.org/10.5281/zenodo.7315105, while methods for building and fitting multivariate Gaussian additive models are provided by the SCM R package, available at https://github.com/VinGioia90/SCM.