Load probability density forecasting by transforming and combining quantile forecasts

Abstract

© 2020 Elsevier Ltd Compared with traditional deterministic load forecasting, probabilistic load forecasting (PLF) help us understand the potential risks in the power system operation by providing more information about future uncertainties of the loads. Quantile forecasting, as a kind of non-parametric probabilistic forecasting method, has been well developed and widely used in PLF. However, the results of quantile forecasts are discrete, which contain fewer details than density forecasts which provide the most comprehensive information. This paper proposes a novel day-ahead load probability density forecasting method by transforming and combining multiple quantile forecasts. The proposed method includes two main steps: transformation and combination. In the first step, the kernel density estimation method is used to transform the individual quantile forecast into the probability density curve; in the second step, an optimization problem is established to obtain the weighted combination of different probability density forecasts. The perturbation search method is applied to determine the optimal weight of each individual forecast. We demonstrate the effectiveness and superiority of our proposed method using comprehensive case studies on the real-world load data from Guangdong province in China, ISO New England (ISO-NE) in the US and Irish smart meter data. Case studies show that the combined model is robust to kernel function selection in the transformation step and has better forecasting performance. Compared with the best individual model, the purposed combined model has an accuracy improvement of 1.54% in the Guangdong dataset and 2.9% in the ISO-NE dataset in terms of the continuous ranked probability score. The proposed combination forecasting method can be robust in high volatility scenarios.