【学术预告】（2024年“逸仙大气论坛”系列讲座第37讲）The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand

报告简介：Substation annual maximum electricity demand events are extreme, as customers respond to infrequent and extreme weather. Despite the extreme nature of annual maximum demand, the statistical theory of extreme values has only rarely, if ever, been applied.  To support long term planning, utilities typically complete energy consumption and maximum demand forecasts, which are often conducted separately through two different process, leading to inconsistent trends and messages.  To address these shortcomings, a point process approach from extreme value theory is proposed to model substation maximum demand as a function of trends in three common factors already required by utilities including customer count, average demand, and installed photovoltaic capacity.  The point process model can be parameterized as a nonstationary generalized extreme value distribution with location and scale parameters dependent on the trends of these factors.  As the generalized extreme value distribution governs the behaviors of block maxima (annual maximum demand) with forecast trends of three common factors, substation maximum demand can be estimated as per quantiles required by planning standards.  Therefore, the proposed approach is not only realistic and flexible to forecast maximum demand but also ensures consistent outcomes and messaging between the two outputs from energy consumption and maximum demand forecasts.

报告人简介：
李云博士是Western Power的高级数据分析顾问兼高级数据科学家，负责为服务标准基准和服务标准目标、电力网络可靠性和运营预测、能源消耗和峰值需求预测开发统计模型，并为战略和运营信息分析提供高级别的内部咨询和数据分析服务。同时，他通过智能建模和对现有业务洞察的综合分析，为Western Power提供高层次的服务。在2015年8月加入Western Power之前，他曾是CSIRO数学、信息学和统计学部的首席研究科学家。他已发表超过50篇研究论文。其研究兴趣包括极端事件的统计建模、统计气候学和降尺度模型、电力消耗和峰值需求预测，以及输配电网络的可靠性。