# Electricity Demand Forecasting by Multi-Task Learning

arXiv: https://arxiv.org/abs/1512.08178 · HTML: https://mlsanity.com/paper/1512.08178/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2015.

## Built on by (most cited first, 5 of 5)

- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data](https://mlsanity.com/paper/2406.16221/) (2024)
- [Multi-task Online Learning for Probabilistic Load Forecasting](https://mlsanity.com/paper/2502.04163/) (2025)
- [TriForecaster: A Mixture of Experts Framework for Multi-Region Electric Load Forecasting with Tri-dimensional Specialization](https://mlsanity.com/paper/2508.09753/) (2025)
- [Adaptive Multi-task Learning for Probabilistic Load Forecasting](https://mlsanity.com/paper/2512.20232/) (2025)

## Abstract (arXiv)

We explore the application of kernel-based multi-task learning techniques to forecast the demand of electricity in multiple nodes of a distribution network. We show that recently developed output kernel learning techniques are particularly well suited to solve this problem, as they allow to flexibly model the complex seasonal effects that characterize electricity demand data, while learning and exploiting correlations between multiple demand profiles. We also demonstrate that kernels with a multiplicative structure yield superior predictive performance with respect to the widely adopted (generalized) additive models. Our study is based on residential and industrial smart meter data provided by the Irish Commission for Energy Regulation (CER).