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

IF 4.3 3区 工程技术 Q2 ENERGY & FUELS
Ajay Upadhaya, Jan-Simon Telle, Sunke Schlüters, Mohammad Saber, Karsten von Maydell
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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.

Abstract Image

一种扩展确定性模型的鲁棒方法,用于不确定性的量化和概率预测的综合评估
发电量和需求预测是电力系统规划、运行和众多决策过程的基础。然而,传统的确定性预测缺乏关于不确定性的关键信息。随着电力系统分散化程度的提高,理解和量化不确定性对于保持恢复力至关重要。本文介绍了不确定性合并方法(UBM),这是一种扩展确定性模型的新方法,可以提供全面的概率预测,从而支持能源管理中的明智决策。UBM具有简单性、低数据需求、最小特征工程、计算效率、适应性和易于实现等优点。它解决了分布式综合本地能源系统,特别是商业设施中对可靠和具有成本效益的能源管理系统(EMS)解决方案的需求。为了验证其实际适用性,对德国北部物流设施的综合能源系统进行了案例研究,重点研究了电力需求、热需求和光伏发电的概率预测。结果表明,UBM具有跨部门的高可靠性。然而,由于确定性模型获得的精度较低,在概率PV发电预测中观察到较低的清晰度。值得注意的是,确定性模型的精度显著影响着UBM的精度。此外,本文通过实现新的评估分数,即图形校准分数,分位数校准分数(QCS)和百分比分位数校准分数(PQCS),解决了概率预测中常用评估分数的各种挑战。在这项工作中提出的发现对提高分布式综合地方能源系统的决策能力有重大贡献。
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来源期刊
International Journal of Energy Research
International Journal of Energy Research 工程技术-核科学技术
CiteScore
9.80
自引率
8.70%
发文量
1170
审稿时长
3.1 months
期刊介绍: The International Journal of Energy Research (IJER) is dedicated to providing a multidisciplinary, unique platform for researchers, scientists, engineers, technology developers, planners, and policy makers to present their research results and findings in a compelling manner on novel energy systems and applications. IJER covers the entire spectrum of energy from production to conversion, conservation, management, systems, technologies, etc. We encourage papers submissions aiming at better efficiency, cost improvements, more effective resource use, improved design and analysis, reduced environmental impact, and hence leading to better sustainability. IJER is concerned with the development and exploitation of both advanced traditional and new energy sources, systems, technologies and applications. Interdisciplinary subjects in the area of novel energy systems and applications are also encouraged. High-quality research papers are solicited in, but are not limited to, the following areas with innovative and novel contents: -Biofuels and alternatives -Carbon capturing and storage technologies -Clean coal technologies -Energy conversion, conservation and management -Energy storage -Energy systems -Hybrid/combined/integrated energy systems for multi-generation -Hydrogen energy and fuel cells -Hydrogen production technologies -Micro- and nano-energy systems and technologies -Nuclear energy -Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass) -Smart energy system
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