Embedding based quantile regression neural network for probabilistic load forecasting

Abstract

Compared to traditional point load forecasting, probabilistic load forecasting (PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year; the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperature forecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

“…An empirical formula was also proposed to select parameters for the temperature scenario generation methods. The idea of generating temperature scenarios was also applied in [131]. An embedding based quantile regression neural network was used as the regression mode instead of MLR model, where the embedding layer can model the effect of calendar variables.…”

Combining Probabilistic Load Forecasts

“…Xie and Hong further compared three temperature scenario generation methods for probabilistic load forecasting in [7]. Researchers have also developed other means to generate probabilistic forecasts, such as residual simulation [8], combining point forecasts [9], and using probabilistic forecasting techniques such as quantile regression [10], [11]. Some works about probabilistic forecasting consider the integration of renewable energy integration [12], [13].…”

Feature selection for probabilistic load forecasting via sparse penalized quantile regression

“…Quantile regression model is the most frequently used tool for PFL. An embedding based quantile regression neural network (QRNN) was proposed in [2] for PLF, where the discrete variables such as day type, day of the week, and hour of the day are modeled by the embedding matrix. Another improved QRNN was presented in [3].…”