Long-term daily peak demand forecast plays an important role in the effective and economic operations and planning of power systems. However, many uncertainties and building demand variability, which are associated with climate and socio-economic changes, complicate demand forecasting and expose power system operators to the risk of failing to meet electricity demand. This study presents a new approach to provide the long-term density prediction of the daily peak demand. Specifically, we make use of temperature projections from physics-based global climate models and calibrate the projections to address possible biases. In addition, the effects of population growth and demand side management efforts in buildings are taken into consideration. Finally, the daily peak demands are modeled with the nonhomogeneous generalized extreme value distribution where the parameters are allowed to vary, depending on the predicted temperature and population. A case study using actual building use data in the south-central region in Texas demonstrates that the proposed approach can quantify the uncertainties in an integrative framework and provide useful insights into the long-term evolution of peak demand density. A well-established building demand saving strategy is predicted to buffer against the growing needs of long-term peak electricity demand.

长期的每日峰值需求预测在电力系统的有效，经济运行和计划中起着重要作用。但是，与气候和社会经济变化相关的许多不确定性和建筑需求的可变性使需求预测变得复杂，并使电力系统运营商面临无法满足电力需求的风险。这项研究提出了一种新的方法，可以提供每日峰值需求的长期密度预测。具体来说，我们利用基于物理学的全球气候模型中的温度预测，并校准预测以解决可能的偏差。此外，还考虑了人口增长的影响以及建筑物中需求侧管理工作的影响。最后，每天的峰值需求是通过非均匀的广义极值分布来建模的，其中，根据预测的温度和总体，允许改变参数。使用德克萨斯州中南部地区的实际建筑使用数据进行的案例研究表明，所提出的方法可以在综合框架中量化不确定性，并为高峰需求密度的长期演变提供有用的见解。预计建立完善的建筑需求节省策略可以缓冲长期峰值用电需求的增长。使用德克萨斯州中南部地区的实际建筑使用数据进行的案例研究表明，所提出的方法可以在综合框架中量化不确定性，并为高峰需求密度的长期演变提供有用的见解。预计建立完善的建筑需求节省策略可以缓冲长期峰值用电需求的增长。使用德克萨斯州中南部地区的实际建筑使用数据进行的案例研究表明，所提出的方法可以在综合框架中量化不确定性，并为高峰需求密度的长期演变提供有用的见解。预计建立完善的建筑需求节省策略可以缓冲长期峰值用电需求的增长。