# Daily peak electrical load forecasting with a multi-resolution approach

arXiv: https://arxiv.org/abs/2112.04492 · HTML: https://mlsanity.com/paper/2112.04492/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Fast calibrated additive quantile regression](https://mlsanity.com/paper/1707.03307/) (2017)
- [Smoothing parameter and model selection for general smooth models](https://mlsanity.com/paper/1511.03864/) (2015)
- [Matrix Neural Networks](https://mlsanity.com/paper/1601.03805/) (2016)
- [Modelling and forecasting daily electricity load curves: a hybrid approach](https://mlsanity.com/paper/1611.08632/) (2016)
- [Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids](https://mlsanity.com/paper/2005.13517/) (2020)

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

- [Comparison of generalised additive models and neural networks in applications: A systematic review](https://mlsanity.com/paper/2510.24601/) (2025)
- [PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting](https://mlsanity.com/paper/2605.21550/) (2026)
- [ConTex: Reformulating Counterfactual Generation For Time Series Forecasting](https://mlsanity.com/paper/2606.18049/) (2026)
- [Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Model](https://mlsanity.com/paper/2609.31806/) (2026)

## Abstract (arXiv)

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are a) a general and formal introduction to the multi-resolution modelling approach, b) a discussion on modelling approaches at different resolutions implemented via Generalised Additive Models and Neural Networks and c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.