The goal of this project is to apply data analytics methods to improve energy efficiency in buildings based on automatic description of monitoring data, prediction of future energy needs, and prescribing measures to reduce energy usage. By continuously collecting real-time monitoring data, the algorithms can automatically improve prediction accuracy and prescribe better decision-making options. Methods such as data mining of monitoring data, forecasting, and simulations are used to create decision-making recommendations to optimize energy efficiency in buildings. By optimizing the energy consumption to the needs of occupants and energy availability from renewable sources, the solution can maximize comfort and reduce energy costs.
UBIMET’s main tasks in this project are:
Analysis and prediction of weather parameters (e.g. temperature, solar radiation, cloud cover, or wind velocity) for the exact location of the building for different spatial and temporal resolutions. The parameters of solar radiation and cloudiness will be improved by the development of a new method for probabilistic short-range forecast of cloud dislocation.
Evaluation of the impact of weather data quality (spatial and temporal resolution, accuracy) on energy savings to determine adequate spatial and temporal resolution. The aim is to see if the costs of calculating more accurate forecasts (on a range of less than 4 km) exceed potential energy savings in buildings. A cost/benefit analysis of higher accuracy of weather data will be performed.
Austrian Institute of Technology (AIT)
Energy Department Vienna University of Technology
Automation Systems Group Caverion Österreich GmbH
bmvit (Austrian Ministry of Transport, Innovation, and Technology) in the ICT of the Future program