Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data

This study was designed to investigate a method for short-term, real-time energy demand prediction to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, a BCVTB (Building Control Virtual Test Bed) was designed. The BCVTB was used to predict daily energy demand, based on four kinds of real-time weather data and two kinds of solar radiation calculations. Weather parameters that were used in a model equation to calculate solar radiation were sourced from weather data of the KMA (Korea Meteorological Administration). After conducting energy demand prediction for four days, it was found that all inputted weather data have an effect on the prediction results. These data were applied to real buildings in order to examine their validity. The information data exchange between real-time weather data and simulation data was carried out fairly through the BCVTB.