A Robust Approach to Extend Deterministic Models for the Quantification of Uncertainty and Comprehensive Evaluation of the Probabilistic Forecasting

Abstract

Forecasting generation and demand forms the foundation of power system planning, operation, and a multitude of decision-making processes. However, traditional deterministic forecasts lack crucial information about uncertainty. With the increasing decentralization of power systems, understanding, and quantifying uncertainty are vital for maintaining resilience. This paper introduces the uncertainty binning method (UBM), a novel approach that extends deterministic models to provide comprehensive probabilistic forecasting and thereby support informed decision-making in energy management. The UBM offers advantages such as simplicity, low data requirements, minimal feature engineering, computational efficiency, adaptability, and ease of implementation. It addresses the demand for reliable and cost-effective energy management system (EMS) solutions in distributed integrated local energy systems, particularly in commercial facilities. To validate its practical applicability, a case study was conducted on an integrated energy system at a logistics facility in northern Germany, focusing on the probabilistic forecasting of electricity demand, heat demand, and PV generation. The results demonstrate the UBM’s high reliability across sectors. However, low sharpness was observed in probabilistic PV generation forecasts, attributed to the low accuracy obtained by the deterministic model. Notably, the accuracy of the deterministic model significantly influences the accuracy of the UBM. Additionally, this paper addresses various challenges in popular evaluation scores for probabilistic forecasting with implementing new ones, namely a graphical calibration score, quantile calibration score (QCS), and percentage quantile calibration score (PQCS). The findings presented in this work contribute significantly to enhancing decision-making capabilities within distributed integrated local energy systems.