Selection of Input Variables for a Thermal Load Prediction Model

Abstract

Forecasting of building thermal loads, without the use of simulation software, can be achieved using data from Building Energy Management Systems (BEMS). Experience in building load prediction using historical data has shown that data analysis is a key factor in order to produce accurate results. This paper examines issues related to the selection of appropriate input variables from wider datasets obtained from BEMS sensors. These variables will be introduced to a new data-driven model, which estimates building space loads. Results indicate that ambient temperature and relative humidity along with solar radiation are the predominant variables that should be considered as input variables to the predictive model.