Probabilistic load forecasts provide comprehensive information about future
load uncertainties. In recent years, many methodologies and techniques have
been proposed for probabilistic load forecasting. Forecast combination, a
widely recognized best practice in point forecasting literature, has never been
formally adopted to combine probabilistic load forecasts. This paper proposes a
constrained quantile regression averaging (CQRA) method to create an improved
ensemble from several individual probabilistic forecasts. We formulate the CQRA
parameter estimation problem as a linear program with the objective of
minimizing the pinball loss, with the constraints that the parameters are
nonnegative and summing up to one. We demonstrate the effectiveness of the
proposed method using two publicly available datasets, the ISO New England data
and Irish smart meter data. Comparing with the best individual probabilistic
forecast, the ensemble can reduce the pinball score by 4.39% on average. The
proposed ensemble also demonstrates superior performance over nine other
benchmark ensembles.Comment: Submitted to IEEE Transactions on Smart Gri
