# High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks

arXiv: https://arxiv.org/abs/2203.03342 · HTML: https://mlsanity.com/paper/2203.03342/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 7 Mar 2022.

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

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond](https://mlsanity.com/paper/2107.06675/) (2021)
- [National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?](https://mlsanity.com/paper/2107.06174/) (2021)
- [Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting](https://mlsanity.com/paper/2107.06268/) (2021)

## Built on by (most cited first, 2 of 2)

- [Comparison of generalised additive models and neural networks in applications: A systematic review](https://mlsanity.com/paper/2510.24601/) (2025)
- [Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Learning](https://mlsanity.com/paper/2203.13886/) (2022)

## Abstract (arXiv)

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is particularly interesting for network operators considering replacing high-resolution monitoring predictive models due to economic considerations. We propose models to predict half-hourly minima and maxima of high-resolution (every minute) electricity load data while model inputs are of a lower resolution (30 minutes). We combine predictions of generalized additive models (GAM) and deep artificial neural networks (DNN), which are popular in load forecasting. We extensively analyze the prediction models, including the input parameters' importance, focusing on load, weather, and seasonal effects. The proposed method won a data competition organized by Western Power Distribution, a British distribution network operator. In addition, we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models' robustness. The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error (RMSE). This holds regarding the competition month and the supplementary evaluation study, which covers an additional eleven months. Overall, our proposed model combination reduces the out-of-sample RMSE by 57.4% compared to the benchmark.