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

Ghareeb, Ahmed
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1490472227466522

Abstract Details

2017, Master of Science (M.S.), University of Dayton, Mechanical Engineering.
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.
Kevin Hallinan (Committee Chair)
Andrew Chiasson (Committee Member)
Zhongmei Yao (Committee Member)
62 p.
Artificial Intelligence; Climate Change; Energy; Engineering; Environmental Engineering; Mechanical Engineering; Statistics
Data mining; energy prediction; energy demand; energy demand forecasting; energy; prediction; forecasting; modeling;

Recommended Citations

Citations

  • Ghareeb, A. (2017). Data mining for University of Dayton campus buildings to predict future demand [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1490472227466522

    APA Style (7th edition)

  • Ghareeb, Ahmed. Data mining for University of Dayton campus buildings to predict future demand. 2017. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1490472227466522.

    MLA Style (8th edition)

  • Ghareeb, Ahmed. "Data mining for University of Dayton campus buildings to predict future demand." Master's thesis, University of Dayton, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1490472227466522

    Chicago Manual of Style (17th edition)

dayton1490472227466522
567
© 2017, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.