Lasso estimation for GEFCom2014 probabilistic electric load forecasting

TL;DR

This paper introduces a probabilistic load forecasting method using lasso estimation within a bivariate time-varying threshold AR model that effectively captures seasonal, holiday, and temperature effects, outperforming benchmarks.

Contribution

The paper presents a novel lasso-based probabilistic load forecasting model that integrates temperature effects and seasonal patterns, validated through empirical studies on GEFCom2014-L data.

Findings

Outperforms benchmark models in GEFCom2014-L

Effectively models temperature and seasonal effects

Demonstrates superior probabilistic load forecasting accuracy

Abstract

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.