New Technologies for Enhancing Energy Efficency in Buildings.
This project proposed the application of new digital technologies to reduce buildings energy consumption and, thus, drive their carbon footprint towards zero. Such a digital transition will be performed using IoT and machine learning algorithms and tested in an existing bioclimatic building located at the campus of the UAL, the CIESOL research center.
- Anomalies detection supported by AR. One passive measure for energy saving in buildings is the proper use of their electric appliances and equipment. To do so, anomalies detection of these items is mandatory to avoid malfunction and expensive faults. Through data-driven and knowledge based techniques is possible to check in real-time the operation of the main subsystems of the building. Besides, this objective will be supported with the use of AR in order to facilitate on-site checks and to reduce the time needed for maintenance tasks.
- Characterization and modeling of the users’ behaviour inside the building. Using an occupancy tracking strategy composed of anchors, tags and cameras is expected to create a system for counting people and estimating their trajectory inside the building. The collected data from this system will be the inputs to machine learning algorithms in order to extract trends and patterns and build models to estimate the users’ behaviour. Good estimations of occupancy counts can be helpful for building simulations and serve as the basis of control systems based on MPC.
- Predictions of the solar irradiation and the production of the photovoltaic panels. As is a current trend in modern buildings, where the use of renewable energies is mandatory for energy saving, the CIESOL building has a photovoltaic field to produce electricity. The production of the photovoltaic field depends on solar irradiation. Thus, two predictions will be made for an enhanced operation of the field: i) from data acquired by a pyranometer and using machine learning algorithms as ANNs. In this case, the solar irradiation will be forecasted with a prediction horizon of minutes and/or hours and, ii) from a video stream acquired by a camera focused on the photovoltaic panels. This will be used to estimate the electricity production depending on the amount of dust onto the photovoltaic panels.
More information: http://ntechbuild.es/