Browsing by Author "Laidi, Roufaida"

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    Electrical Energy Consumption Control in Buildings Using Wireless Sensor
    (CERIST, 2018-01-25) Djenouri, Djamel; Laidi, Roufaida; Zizoua, Cherif
    Energy consumption in residential and commercial buildings has increased dramatically worldwide in the last decade, due to the constant population and economic growth, the proliferation of electronic and consumer appliances. This has dramatic footprint on the environment in terms of carbon emission, in addition to the economic impact. Green and smart building strategies will play a pivotal role to reduce this footprint and maximize economic and environmental performance. These strategies can be integrated into buildings at any stage, from design and construction, to maintenance and renovation. The use of modern Information and Communication Technologies (ICT), notably IoT solutions, for building control is one of the promising strategies for the future. The aim of this project was to explore this domain, and as a first step to develop a wireless sensor networks based solution for monitoring and energy management in offices. A prototype has been targeted as a proof of concept where sensors monitor physical parameters in CERSIT offices (presence of people, ambient light, etc.), and accordingly actuate lighting, air conditioning, etc. This report is a short summery of the different parts developed in this project.
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    Machine Learning for Smart Building Applications: Review and Taxonomy
    (ACM, 2019-03) Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko
    The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The paper ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.
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    Machine Learning for Smart Building Applications: Review and Taxonomy
    (ACM, 2019-03) Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko
    The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The paper ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.
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    Multiple Benefits through Smart Home Energy Management Solutions—A Simulation-Based Case Study of a Single-Family-House in Algeria and Germany
    (mdpi, 2019-04-23) Ringel, Marc; Laidi, Roufaida; Djenouri, Djamel
    From both global and local perspectives, there are strong reasons to promote energy efficiency. These reasons have prompted leaders in the European Union (EU) and countries of the Middle East and North Africa (MENA) to adopt policies to move their citizenry toward more efficient energy consumption. Energy efficiency policy is typically framed at the national, or transnational level. Policy makers then aim to incentivize microeconomic actors to align their decisions with macroeconomic policy. We suggest another path towards greater energy efficiency: Highlighting individual benefits at microeconomic level. By simulating lighting, heating and cooling operations in a model single-family home equipped with modest automation, we show that individual actors can be led to pursue energy efficiency out of enlightened self-interest. We apply simple-to-use, easily, scalable impact indicators that can be made available to homeowners and serve as intrinsic economic, environmental and social motivators for pursuing energy efficiency. The indicators reveal tangible homeowner benefits realizable under both the market-based pricing structure for energy in Germany and the state-subsidized pricing structure in Algeria. Benefits accrue under both the continental climate regime of Germany and the Mediterranean regime of Algeria, notably in the case that cooling energy needs are considered. Our findings show that smart home technology provides an attractive path for advancing energy efficiency goals. The indicators we assemble can help policy makers both to promote tangible benefits of energy efficiency to individual homeowners, and to identify those investments of public funds that best support individual pursuit of national and transnational energy goals.
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    UDEPLOY: User-Driven Learning for Occupancy Sensors DEPLOYment In Smart Buildings
    (IEEE, 2018-03) Laidi, Roufaida; Djenouri, Djamel
    A solution for motion sensors deployment in smart buildings is proposed. It differentiates the monitored zones according to their occupancy, where highly-occupied zones have higher coverage requirements over low-occupied zones, and thus are assigned higher granularity in the targeted coverage (weights). The proposed solution is the first that defines a user-driven approach, which uses sampling of occupants’ behavior to determine the zones and the coverage weights. The samples are acquired during a short learning phase and then used to derive a graph model. The latter is plugged into a greedy, yet effective, algorithm that seeks optimal placement for maximizing detection accuracy while reducing the cost (number of sensors). Practical aspects such as the scalability and the applicability of the solution are considered. A simulation study that compares the proposed solution with two state-of-the-art solutions shows the superiority of the proposed approach in the accuracy of detection (increased coverage), and scalability (reduced runtime).
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    UDEPLOY: User-Driven Learning for Occupancy Sensors DEPLOYment In Smart Buildings
    (CERIST, 2017-12-25) Laidi, Roufaida; Djenouri, Djamel
    A solution for motion sensors deployment in smart buildings is proposed. It diferentiates the monitored zones according to their occupancy, where highly-occupied zones have higher coverage requirements over low-occupied zones, and thus are assigned higher granularity in the targeted coverage (weights). The proposed solution is the rst that de nes a user-driven approach, which uses sampling of occupants' behavior to determine the zones and the coverage weights. The samples are acquired during a short learning phase and then used to derive a graph model. The latter is plugged into a greedy, yet e ective, algorithm that seeks optimal placement for maximizing detection accuracy while reducing the cost (number of sensors). Practical aspects such as the scalability and the applicability of the solution are considered. A simulation study that compares the proposed solution with two state-of-the-art solutions shows the superiority of the proposed approach in the accuracy of detection (increased coverage), and scalability (reduced runtime).