HOLSYS
The main idea behind this project was to develop a smart building energy management system using IoT and Big Data technologies. More precisely, we aimed to develop a holistic platform for big data streams acquisition and processing using real-time machine learning algorithms. The platform services were developed and used for efficient optimization of energy consumption according to actual and predicted energy production and electricity consumption. In fact, Big data and IoT technologies (for advanced metering) together with real-time machine learning were combined for timely analyzing data and events’ streams and predicting actual demands (i.e., consumption) and renewable power generation (i.e., production).
The work presented in this report briefly summarizes the main outcomes related to the activities that we have conducted during the project lifecycle.
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RECENT publications
- A. Berrabah, Z. Bouhssine, A. El Maakoul, A. Degiovanni, M. Bakhouya, Towards a Quadrupole-based Method for Buildings Simulation: Validation with ASHRAE 140 Standard, Thermal Science and Engineering Progress 28, 101069, 2022.
- H. Elkhoukhi, M. Bakhouya, D. El Ouadghiri, M. Hanifi, Using Stream Data Processing for Real-Time Occupancy Detection in Smart Buildings, Sensors 22 (6), 2371, 2022.
- Y. Alidrissi, R. Ouladsine, A. Elmouatamid, R. Errouissi, M. Bakhouya, Constant Power Load Stabilization in DC Microgrids Using Continuous-Time Model Predictive Control, Electronics 11 (9), 14813, 2022.
- A. Kharbouch, A. Berouine, H. Elkhoukhi, S. Berrabah, M. Bakhouya, D. El Ouadghiri, J. Gaber, Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation, Sensors 22 (20), 7978, 2022.
- A. Berouine, R. Ouladsine, M. Bakhouya, M. Essaaidi, A Predictive Control Approach for Thermal Energy Management in Buildings, Energy Reports 8, 9127-9141, 2022.