Data-Driven Smart Buildings
DDSB
Several buildings on the Technische Universität Graz (TU Graz) campus were used as case study within the Austrian national project “Cool-Quarter-Plus” to develop and test machine learning methods to predict energy consumption and occupancy, based on indoor air quality measurements and external weather data to inform intelligent cooling control strategies.
Machine Learning (ML) methods are used to predict energy consumption and occupancy.
Information about the building; hourly energy consumption; occupancy for 3 offices (CO₂, number of occupants, temperature, relative humidity); weather data (temperature, solar radiation, …). Data are continuously updated.
The project aims at coordinating cooling concepts at district level. It focuses on an office and research campus. Several methods for predicting energy consumption were developed and compared as part of the project, including random forest-based prediction models and neural network time series models. One major line of work is occupancy prediction based on CO₂, temperature, relative humidity, etc. A paper on this subject is currently under developement.
T. Schranz, J. Exenberger, C. Møldrup Legaard, J. Drgona, G. Schweiger. (2021). Energy prediction under changed demand conditions: robust machine learning models and input feature combinations. Proceedings of Building Simulation 2021: 17th Conference of IBPSA.
https://doi.org/10.26868/25222708.2021.30806
