Publications
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Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review
Heating, Ventilation and Air Conditioning (HVAC) systems in buildings are a major source of global operational CO2 emissions, primarily due to their high energy demands. Traditional controllers have shown effectiveness in managing building energy use. However, they either struggle to handle complex environments or cannot incorporate learning from experience into their decision-making processes, leading to […]
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Impact of polypropylene fibers on the rheological, mechanical, andthermal properties of self-compacting concrete
The objective of this experimental investigation is to examine the impact of using polypropylene fibers on the properties of self-compacting concrete (SCC). Five mixtures were prepared, one reference concrete (without fibers) and four other SCC containing, 0.05,0.1, 0.15, and 0.2% of polypropylenes fibers. Rheological (slump flow, yield stress, and plastic viscosity) and mechanical (compressivestrength) properties […]
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LEAN-BIM SYNERGY IN THE CONSTRUCTION DESIGN PHASE: AUTO-GENERATION AND EVALUATION OF THERMAL ALTERNATIVES
This study explores the integration of Lean principles with Building Information Modeling (BIM) to enhance decision-making in the relatively unexplored field of thermal design for construction projects. Recognizing the limitations of current design processes, characterized by insufficient alternatives and a lack of team collaboration, we introduce a new decision-making tool. This tool centers on a […]
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Sampled-Data Observer Design for Linear Kuramoto-Sivashinsky Systems with Non-Local Output
The aim of this paper is to provide a novel systematic methodology for the design of sampled-data observers for Linear Kuramoto-Sivashinsky systems (LK-S) with non-local outputs. More precisely, we extend the systematic sampled-data observer design approach which is based on the use of an Inter-Sample output predictor to the class of LK-S systems. By using […]
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Enhanced multi-horizon occupancy prediction in smart buildings using cascaded Bi-LSTM models with integrated features
Accurate occupancy prediction in smart buildings is crucial for optimizing energy management, improving occupant comfort, and effectively controlling building systems, particularly for short- and long-term horizons. Recently, deep learning-based occupancy prediction methods have gained considerable attention. However, the full potential of these methods remains under explored in terms of model architecture variations and prediction horizons. […]
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Optimizing Comfort and Energy Efficiency: The Impact of Model Accuracy on Multi-Objective MPC
Abstract—Buildings are responsible for ∼30% of primary energy consumption, mainly because of Heating, Ventilation, and Air Conditioning (HVAC) systems. The usual ON/OFF controller tends to react to occupancy presence, causing discomfort and energy waste. Furthermore, these controllers usually focus on thermal comfort and disregard other comforts, such as air quality, visual, etc. due to their […]
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Optimizing Comfort and Energy Efficiency: The Impact of Model Accuracy on Mutli-Objective MPC
Buildings are responsible for ∼30% of primary energy consumption, mainly because of Heating, Ventilation, and Air Conditioning (HVAC) systems. The usual ON/OFF controller tends to react to occupancy presence, causing discomfort and energy waste. Furthermore, these controllers usually focus on thermal comfort and disregard other comforts, such as air quality, visual, etc. due to their […]
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Metaheuristics and Machine Learning Convergence: A Comprehensive Survey and Future Prospects
The integration of machine learning techniques with optimization algorithms has garnered increasing interest in recent years. Two primary purposes emerge from the literature: leveraging metaheuristics in machine learning applications such as regression, classification, and clustering, and enhancing metaheuristics using machine learning to improve convergence time, solution quality, and flexibility. Machine learning techniques offer real-time decision-making […]
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Industrial Process Optimization with Digital Twin Solutions: Modeling, Monitoring and Decision-Making. Application to a water filtration unit.
The digital transformation of industry is an essential pillar of the global economy, aimed at improving productivity, promoting sustainable development and optimizing industrial performance. It relies on the use of advanced technologies such as cyber-physical systems, artificial intelligence and the Internet of Things, which make industrial systems not only more complex, but also more communicative, […]
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Supply Chain Optimization with the Digital Twin: Case study of a warehouse
In a complex logistics landscape, the need for efficient and optimized Supply Chain’s (SC’s) is becoming increasingly important. This is crucial to creating an efficient SC and ensuring consistent, optimized use of resources. Efficient logistics management is essential if companies are to maintain their competitiveness in the marketplace. Digital technologies, such as digital warehouses, are […]
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Medical Compression Prototype in a Wireless Sensor network
Medical Compression Prototype in a Wireless Sensor network (Submitted).
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Hyperparameter Impact on Computational Efficiency in Federated Edge Learning
The heterogeneity induced by the federated edge learning execution environment poses many performance challenges. Indeed, a balance between efficient resource usage and inference accuracy must be found. Our work therefore aims at characterizing the hyperparameter influence by creating a variety of simulated execution circumstances. We designed an experimentation platform to simulate the execution of a […]
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