Single and Multivariate Regression Models for Estimating Monthly Energy Consumption in Schools in Hot and Humid Climates

摘要

Modeling of energy use in existing buildings is required for different applications such as building energy management, establishing baselines, and calculating retrofit savings. A single or multivariate regression model is generally used as a means of developing these models of energy consumption in buildings. This article explores the application of single and multivariate models in estimation of monthly energy consumption in schools and discusses several aspects of model improvement. Several regression models, ranging from a simple outdoor-only, dry-bulb temperature, to multivariate are studied to find a comprehensive methodology in estimating energy consumption at schools in hot and humid climates. Data on energy consumption in 225 schools selected from four counties in Central Florida were collected to evaluate the models and to provide recommendations for improvements. The results show that by including school scheduling in the model, the accuracy in monthly energy consumption prediction is significantly improved, compared to that using only temperature regression. There are no improvements in the mode prediction by adding parameters such as temperature change point or input variables such as ambient air dew point temperature.