# Efficient mid-term forecasting of hourly electricity load using generalized additive models

arXiv: https://arxiv.org/abs/2405.17070 · HTML: https://mlsanity.com/paper/2405.17070/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 74 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 7 of 7)

- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France](https://mlsanity.com/paper/2009.06527/) (2020)
- [Adaptive Probabilistic Forecasting of Electricity (Net-)Load](https://mlsanity.com/paper/2301.10090/) (2023)
- [Lasso estimation for GEFCom2014 probabilistic electric load forecasting](https://mlsanity.com/paper/1603.01376/) (2016)
- [Changes in electricity demand pattern in Europe due to COVID-19 shutdowns](https://mlsanity.com/paper/2004.14864/) (2020)
- [Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting](https://mlsanity.com/paper/2107.06268/) (2021)
- [Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load Forecasting](https://mlsanity.com/paper/1809.03561/) (2018)

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

- [AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting](https://mlsanity.com/paper/2503.24019/) (2025)
- [Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Model](https://mlsanity.com/paper/2609.31806/) (2026)

## Abstract (arXiv)

Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, planning and building energy storage systems, and energy trading. While numerous models effectively predict short-term (hours to a few days) hourly load, mid-term forecasting solutions remain scarce. In mid-term load forecasting, capturing the multifaceted characteristics of load, including daily, weekly and annual seasonal patterns, as well as autoregressive effects, weather and holiday impacts, and socio-economic non-stationarities, presents significant modeling challenges. To address these challenges, we propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines that is enhanced with autoregressive post-processing. This model incorporates smoothed temperatures, Error-Trend-Seasonal (ETS) modeled and persistently forecasted non-stationary socio-economic states, a nuanced representation of effects from vacation periods, fixed date and weekday holidays, and seasonal information as inputs. The proposed model is evaluated using load data from 24 European countries over more than 9 years (2015-2024). This analysis demonstrates that the model not only has significantly enhanced forecasting accuracy compared to state-of-the-art methods but also offers valuable insights into the influence of individual components on predicted load, given its full interpretability. Achieving performance akin to day-ahead Transmission System Operator (TSO) forecasts, with computation times of just a few seconds for several years of hourly data, underscores the potential of the model for practical application in the power system industry.