Data-driven Uncertainty Quantification and Characterization for Household Energy Demand Across Multiple Time-scales

Uncertainty has become a key challenge in energy profiles at the domestic level, which is influenced by a variety of factors, such as behaviours, technologies, weather conditions, energy prices, and so on. These factors influence the household demand at various time horizons, hence result in diverse uncertainty natures across time-scales. Therefore, it is crucial to understand the temporal natures of the demand uncertainty at different time-scales, particularly intra-day and inter-days. This paper firstly attempts to quantify and characterize uncertainties of household electricity demand across multiple time scales. An advanced Data-driven Temporal-dependency Haar expansions Uncertainty Quantification (DTHUQ) approach is thus proposed by this paper. The proposed approach consists of two stages. At first stage, uncertainty will be decomposed into multiple components of different time scales and then quantified by Monte Carlo methods. The second stage will further characterize natures of each uncertainty component in terms of threefold: i) temporal uncertainty distribution at a certain time scale; ii) coupling degree between two time scales; iii) uncertainty propagation natures to the system level of uncertainty component at a certain time scale. The proposed analysis is demonstrated on Irish smart meter database. To prove the efficiency and validity of the proposed approach, the error bound and computational cost is analyzed.