  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.
