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Combining Probabilistic Load Forecasts
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DOI：10.1109/TSG.2018.2833869.png)
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Abstract
En 中文
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 and 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 439% on average. The proposed ensemble also demonstrates superior performance over nine other benchmark ensembles.
Keywords:
Probabilistic load forecasting
quantile regression
pinball loss function
ensemble method
linear programming
forecasts combination
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