# Hierarchical transfer learning with applications for electricity load forecasting

arXiv: https://arxiv.org/abs/2111.08512 · HTML: https://mlsanity.com/paper/2111.08512/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Second-order Bound with Excess Losses](https://mlsanity.com/paper/1402.2044/) (2014)
- [Learning in High Dimension Always Amounts to Extrapolation](https://mlsanity.com/paper/2110.09485/) (2021)
- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [Fast calibrated additive quantile regression](https://mlsanity.com/paper/1707.03307/) (2017)
- [Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France](https://mlsanity.com/paper/2009.06527/) (2020)
- [Online Hierarchical Forecasting for Power Consumption Data](https://mlsanity.com/paper/2003.00585/) (2020)
- [Transfer Learning for Linear Regression: a Statistical Test of Gain](https://mlsanity.com/paper/2102.09504/) (2021)
- [Hierarchical forecasting with a top-down alignment of independent level forecasts](https://mlsanity.com/paper/2103.08250/) (2021)
- [Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting](https://mlsanity.com/paper/2107.06268/) (2021)
- [Additive stacking for disaggregate electricity demand forecasting](https://mlsanity.com/paper/2005.10092/) (2020)
- [State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting](https://mlsanity.com/paper/2110.00334/) (2021)

## Built on by (most cited first, 4 of 4)

- [Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models](https://mlsanity.com/paper/2302.08192/) (2023)
- [Graph Neural Networks for Electricity Load Forecasting](https://mlsanity.com/paper/2507.03690/) (2025)
- [Cascaded Transfer: Learning Many Tasks under Budget Constraints](https://mlsanity.com/paper/2601.21513/) (2026)
- [RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting](https://mlsanity.com/paper/2606.02852/) (2026)

## Abstract (arXiv)

The recent abundance of data on electricity consumption at different scales opens new challenges and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales. In this work, we take advantage of the similarity between this hierarchical prediction problem and multi-scale transfer learning. We develop two methods for hierarchical transfer learning, based respectively on the stacking of generalized additive models and random forests, and on the use of aggregation of experts. We apply these methods to two problems of electricity load forecasting at national scale, using smart meter data in the first case, and regional data in the second case. For these two usecases, we compare the performances of our methods to that of benchmark algorithms, and we investigate their behaviour using variable importance analysis. Our results demonstrate the interest of both methods, which lead to a significant improvement of the predictions.