Probabilistic net load forecasting using sensor-driven data and dual-coefficient network

Abstract

With the widespread deployment of sensor networks in modern energy systems, large-scale time-series data from distributed sources (e.g., load demand, photovoltaic generation, and wind power) provide new opportunities for intelligent system perception and predictive modeling. Accurate and uncertainty-aware net load modeling plays a critical role in supporting fault diagnosis, risk assessment, and fault-tolerant control. To overcome the deficiencies of current approaches in representing uncertainty, distribution shift, and quantile crossing, the present study develops a novel probabilistic framework for net load forecasting. It employs Dual-coefficient network (DUCET) to model both input-space and output-space distribution characteristics, thereby enhancing robustness against distribution shifts. Furthermore, a quantile loss–driven Informer architecture is adopted for modeling temporal dependencies. It can also generate probabilistic forecasts through conditional quantiles. To resolve the quantile crossing problem commonly observed in quantile regression, a quantile reconstruction strategy (QRS) is introduced, which reconstructs a coherent predictive distribution using kernel density estimation and Gaussian approximation. Experimental results on real-world net load data from Austria show that the proposed probabilistic framework surpasses benchmark methods. Additional ablation experiments confirm the validity of DUCET and QRS in improving forecasting accuracy and reliability.