Data mining for University of Dayton campus buildings to predict future demand

Date of Award


Degree Name

M.S. in Mechanical Engineering


Department of Mechanical and Aerospace Engineering


Advisor: K. P Hallinan


The ability to forecast demand for large facilities will be increasingly important as real-time power pricing scenarios become increasingly present. Accurate prediction will inform data-driven power shedding to reduce energy costs most effectively with minimal sacrifice of comfort. A number of previous researchers have researched this topic, achieving results with varying amount of success. This study looks to forecast demand for a university complex of buildings, subject to the unique occupancy variation of such institutions. Specifically addressed is the use of academic institutional data associated with temporal enrollment and the academic calendar. As well, it addresses use of demand data in all buildings in an effort to more accurately predict this aggregate demand of the university. A data mining based approach based upon a Random Forest regression tree algorithm is used to develop the forecast model. The mean absolute percentage error (MAPE) value associated with the model applied to a validation set of data is on the order of 2.21 % based upon actual weather data. Using forecasted weather data, the MAPE increases to approximately 6.65% in predicted day-ahead demand.


University of Dayton Planning, College buildings Energy consumption Case studies, Energy auditing, Energy conservation Planning, Energy, Engineering, Mechanical Engineering, Statistics, Environmental Engineering, Climate Change, Artificial Intelligence, Data mining, energy prediction, energy demand, energy demand forecasting, prediction, forecasting, modeling

Rights Statement

Copyright © 2017, author