A team of students led by the Data and Analytics Practice at OIT will develop a robust forecasting model for predicting energy usage for different facilities on campus. Students will explore a wide range of real-world time-series data challenges from anomaly detection as well as handling, to benchmarking traditional statistical and modern machine learning models for forecasting. Students will also gain valuable experience developing an interactive application with latest open source libraries converting Jupyter notebooks into web applications to facilitate effective stakeholder collaboration. This work will enable several critical analyses for Duke Facilities Management to optimize their operations and significantly reduce costs.
Project Manager: Billy Carson