  Accurate and reliable electricity load forecasts are becoming increasingly
important as the share of intermittent resources in the system increases.
Distribution System Operators (DSOs) are called to accurately forecast their
production and consumption to place optimal bids in the day-ahead market.
Violations of their dispatch-plan requires activation of reserve-power which
has a direct cost for the DSO, and also necessitate available reserve-capacity.
Forecasts must account for the volatility of weather-parameters that impacts
both the production and consumption of electricity. If DSO-loads are small or
lower-granularity forecasts are needed, traditional statistical methods may
fail to provide reliable performance since they rely on a priori statistical
distributions of the variables to forecast. In this paper we introduce a
probabilistic load forecast (PLF) method based on empirical copulas. Our model
is data-driven, does not need a priori assumption on parametric distribution
for variables, nor the dependence structure (copula), but employs a kernel
density estimate of the underlying distribution using beta kernels that have
bounded support on the unit hypercube. The method naturally supports variables
with widely different distributions, such as weather data (including forecasted
ones) and historic electricity consumption, and produces a conditional
probability distribution for every time step in the forecast, which allows
inferring the quantiles of interest. The proposed non-parametric approach is
highly flexible and can produce meaningful forecasts even at very low
aggregated levels (e.g. neighborhoods). We present results from an open dataset
and showcase the strength of the model with respect to Quantile Regression
using standard probabilistic evaluation metrics.
