论文标题
电加热分解的贝叶斯模型
Bayesian model of electrical heating disaggregation
论文作者
论文摘要
采用智能电表是欧洲向智能能源过渡的道路的主要里程碑。法国的住宅领域代表$ \ $ \ $ 35 \％的电力消耗，使用$ \ $ \ $ 40 \％（insee）的家庭使用电气加热。预计部署的智能电表链接的数量预计在2021年将达到35m。在本手稿中，我们对676个家庭进行了分析，其观察期至少为6个月，为此，我们已经拥有元数据，例如建造年份，以及供暖年份的类型，并提出了一种贝叶斯的贝叶斯在温度下的贝叶斯式消耗，以使供应供应供应供电，从而使供应无效。本质上，模型是零件线性模型的混合物，其特征在于温度阈值，在下面，我们允许两种模式的混合物代表潜在状态/外出。
Adoption of smart meters is a major milestone on the path of European transition to smart energy. The residential sector in France represents $\approx$35\% of electricity consumption with $\approx$40\% (INSEE) of households using electrical heating. The number of deployed smart meters Linky is expected to reach 35M in 2021. In this manuscript we present an analysis of 676 households with an observation period of at least 6 months, for which we have metadata, such as the year of construction and the type of heating and propose a Bayesian model of the electrical consumption conditioned on temperature that allows to disaggregate the heating component from the electrical load curve in an unsupervised manner. In essence the model is a mixture of piece-wise linear models, characterised by a temperature threshold, below which we allow a mixture of two modes to represent the latent state home/away.
