Structured stochastic models based on multi-source heterogeneous data for predicting internal electricity load of non-residential buildings

Abstract

Electricity load patterns in buildings are increasingly diverse and inherently present building-by-building and temporal variations. This study develops structured stochastic models to capture individual and temporal variations in non-residential building groups (e.g., education, office, culture, and retail buildings). Hourly electricity load data of non-residential buildings, categorized by building use, are sourced from two open datasets (Ministry of Land, Infrastructure and Transport and Korea Electric Power Corporation). First, heterogeneous public datasets are processed and utilized to provide both detailed building information and high-resolution electricity load data. Second, a structured set of four stochastic models are formulated using linear regression and multivariate normal distribution models. Model 1 utilizes the beta distribution to derive daily average electricity load per unit area for each building use. Model 2 utilizes the burr distribution to determine the daily variation of electricity load from the daily average. Models 3 and 4 utilize a multivariate normal distribution to represent the hourly average electricity load profile and its time variation, respectively. The coefficient of variation of the root mean square error (CVRMSE) for the structured probabilistic models ranges from 5.2 % to 14.8 % regarding five key load characterization variables. CVRMSEs for near-peak load, near-base load, rise time, high-load duration, and fall time are 8.5–13.9, 9.5–14.7, 7.3–14.8, 6.4–13.9, and 5.2–11.4 %, respectively. The validation results demonstrate that the proposed probabilistic models capture actual variations in measurements. Structured statistical models improve prediction performance by distinctly reflecting individual and temporal variations in electricity loads.