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 mostly used as a means of developing these models of energy consumption in buildings. This paper explores the applications of single and multivariate models on estimation of monthly energy consumptions in schools and discusses the several aspects of model improvements. Several regression models ranging from a simple only outdoor dry-bulb temperature to multivariate models are studied to find a comprehensive methodology in estimating energy consumptions in schools located in hot and humid climates. Data on energy consumptions in 225 schools selected from four different counties in central Florida are collected to evaluate the models and provide recommendations for improvements. The results show that by including the school scheduling in the model, the accuracy in the monthly energy consumption prediction is significantly improved comparing tothat when using only temperature regression model.