Bidong Liu

TL;DR: A practical methodology to generate probabilistic load forecasts by performing quantile regression averaging on a set of sister point forecasts and it leads to dominantly better performance as measured by the pinball loss function and the Winkler score.

TL;DR: In this paper, the authors proposed a probabilistic load forecasting method based on Quantile Regression Averaging (QRA) on a set of sister point forecasts, which can leverage the development in the point load forecasting literature over the past several decades.

TL;DR: A comprehensive study to model the recency effect using a big data approach and two interesting findings are presented: 1) the naive models are not useful for benchmark purposes in load forecasting at aggregated level due to their lack of accuracy; and 2) slicing the data into 24 pieces to develop one model for each hour is not necessarily better than building one interaction regression model using all 24 hours together.

TL;DR: This paper investigates the performance of combining so-called sister load forecasts with eight methods: three variants of arithmetic averaging, four regression based and one performance based method.

TL;DR: In this paper, the authors investigated the performance of combining sister load forecasts with eight methods: three variants of arithmetic averaging, four regression based and one performance based method, and demonstrated that combing sister forecasts outperforms the benchmark methods significantly in terms of forecasting accuracy measured by Mean Absolute Percentage Error.