A Symmetric Block Resampling Method to Generate Energy Time Series Data

Energy modeling frequently relies on time series data, whether observed or forecasted. This is particularly the case, for example, in capacity planning models that use hourly production and load data forecasted to occur over the coming several decades. This paper addresses the attendant problem of performing sensitivity, robustness, and other post-solution analyses using time series data. We propose an efficient and relatively simple method, which we call the symmetric block resampling method, a non-parametric bootstrapping approach, for generating arbitrary numbers of time series from a single observed or forecast series. The paper presents and assesses the method. We find that the generated series are both visually and by statistical summary measures close to the original observational data. In consequence these series are credibly taken as stochastic instances from a common distribution, that of the original series of observations. We find as well that the generated series induce variability in properties of the series that are important for energy modeling, in particular periods of under-and over-production, and periods of increased ramping rates. In consequence, series produced in this way are apt for use in robustness, sensitivity, and in general post-solution analysis of energy planning models. These validity factors auger well for applications beyond energy modeling.