# Lasso estimation for GEFCom2014 probabilistic electric load forecasting

arXiv: https://arxiv.org/abs/1603.01376 · HTML: https://mlsanity.com/paper/1603.01376/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2016.

## Built on by (most cited first, 7 of 7)

- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting](https://mlsanity.com/paper/2107.06268/) (2021)
- [Efficient mid-term forecasting of hourly electricity load using generalized additive models](https://mlsanity.com/paper/2405.17070/) (2024)
- [Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach](https://mlsanity.com/paper/2206.15192/) (2022)
- [Interpretable Load Forecasting via Representation Learning of Geo-distributed Meteorological Factors](https://mlsanity.com/paper/2501.02241/) (2025)
- [HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting](https://mlsanity.com/paper/2501.10637/) (2025)
- [Locally Adaptive Multi-Objective Learning](https://mlsanity.com/paper/2602.14952/) (2026)

## Abstract (arXiv)

We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach incorporates the temperature effects directly, and reflects daily, weekly, and annual seasonal patterns and public holiday effects. We provide two empirical studies, one based on the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014 (GEFCom2014-L), and the other based on another recent probabilistic load forecasting competition that follows a setup similar to that of GEFCom2014-L. In both empirical case studies, the proposed methodology outperforms two multiple linear regression based benchmarks from among the top eight entries to GEFCom2014-L.