Density Forecasting for Long-Term Peak Electricity Demand
Density Forecasting for Long-Term Peak Electricity Demand
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DOI：
10.1109/tpwrs.2009.2036017
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发表时间：
2010-05-01
影响因子：
6.6
通讯作者：
Fan, Shu
中科院分区：
文献类型：
--
作者：
Hyndman, Rob J.;Fan, Shu
长期电力需求预测在规划未来发电设施和输电扩展方面起着重要作用。从长期来看，规划者必须对潜在的高峰需求水平采取概率观点。因此，密度预测（提供需求未来可能值的全概率分布的估计）比点预测更有帮助，并且对于公用事业公司评估和对冲需求变化和预测不确定性所产生的财务风险是必要的。本文提出了一种新的方法来预测长期的高峰电力需求密度，在一个给定的季节高峰电力需求受到一系列的不确定性，包括潜在的人口增长，不断变化的技术，经济条件，主要的天气条件（和这些条件的时间），以及在个人使用固有的一般随机性。它也受到一些已知的日历效应，由于一天中的时间，一周中的一天，一年中的时间，和公共假日。首先，半参数加性模型被用来估计需求和驱动变量之间的关系，包括温度，日历效应和一些人口和经济变量。然后，通过使用温度模拟、假设的未来经济情景和残差自举的混合来预测需求分布。温度模拟是通过一种新的季节性自举方法与可变块实现的。所提出的方法已被用来预测南澳自2007年以来的年和周峰值电力需求的概率分布。通过将预测结果与2007-2008年夏季的实际需求进行比较，评估了该方法的性能。
Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long-term context, planners must adopt a probabilistic view of potential peak demand levels. Therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand.Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays.A comprehensive forecasting solution is described in this paper. First, semi-parametric additive models are used to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then the demand distributions are forecasted by using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks.The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. The performance of the methodology is evaluated by comparing the forecast results with the actual demand of the summer 2007-2008.