Load forecasting is an integral part of power system operations and planning. Due to rising penetrations of rooftop PV, electric vehicles and demand response programs, forecasting the load of individual and a small group of households is becoming increasingly important. Forecasting the load accurately, however, is considerably more difficult when only a few households are included. A way to mitigate this challenge is to provide a set of scenarios instead of one point forecast, so an operator or utility can consider the full range of behaviors. This paper proposes a novel scenario forecasting approach for residential load using flow-based conditional generative models. Compared to existing scenario forecasting methods, our approach can generate scenarios that are not only able to infer possible future realizations of residential load from the observed historical data but also realistic enough to cover a wide range of behaviors. Particularly, the flow-based models utilize a flow of reversible transformations to maximize the value of conditional density function of future load given the past observations. In order to better capture the complex temporal dependency of the forecasted future load, we extend the structure design for the reversible transformations in flow-based conditional generative models by strengthening the coupling between the output and the conditioning input. The simulation results show the flow-based designs outperform existing methods in scenario forecasting for residential load by providing both more accurate and more diverse scenarios.

中文翻译：

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住宅负荷曲线的情景预测

负荷预测是电力系统运行和规划的一个组成部分。由于屋顶光伏、电动汽车和需求响应计划的渗透率不断提高，预测个人和一小部分家庭的负荷变得越来越重要。然而，当只包括几个家庭时，准确预测负荷要困难得多。缓解这一挑战的一种方法是提供一组场景而不是单点预测，因此运营商或公用事业公司可以考虑所有行为。本文提出了一种使用基于流量的条件生成模型的住宅负荷情景预测方法。与现有的情景预测方法相比，我们的方法可以生成场景，这些场景不仅能够从观察到的历史数据中推断出未来住宅负荷的可能实现，而且还足够现实以涵盖广泛的行为。特别是，基于流的模型利用可逆变换流来最大化给定过去观察的未来负载的条件密度函数值。为了更好地捕捉预测未来负载的复杂时间依赖性，我们通过加强输出和条件输入之间的耦合，扩展了基于流的条件生成模型中可逆变换的结构设计。仿真结果表明，通过提供更准确和更多样化的场景，基于流的设计在住宅负荷的场景预测中优于现有方法。