An optimal approach for electrical management problem in dwellings

Ha, Duy Long; Joumaa, Hussein; Ploix, Stéphane; Jacomino, Mireille

doi:10.1016/j.enbuild.2011.11.027

Cited by 52 publications

(22 citation statements)

References 13 publications

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“…In contrast, Ha et al [8] propose a categorization that focuses on the services that are provided by the devices. The first dimension is similar to the previous categorization and targets on time and availability (permanent services versus temporary or timed services).…”

Section: Categorization Of Devices In Buildingsmentioning

confidence: 99%

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

Braun

Mauser

Schmeck

2016

Applications of Evolutionary Computation

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Abstract. The optimization of operating times and operation modes of devices and systems that consume or generate electricity in buildings by building energy management systems promises to alleviate problems arising in today's electricity grids. Conflicting objectives may have to be pursued in this context, giving rise to a multi-objective optimization problem. This paper presents the optimization of appliances as well as heating and air-conditioning devices in two distinct settings of smart buildings, a residential and a commercial building, with respect to the minimization of energy costs, CO2 emissions, discomfort, and technical wearout. We propose new encodings for appliances that are based on a combined categorization of devices respecting both, the optimization of operating times as well as operation modes, e.g., of hybrid devices. To identify an evolutionary algorithm that promises to lead to good optimization results of the devices in our real-world lab environments, we compare four state-of-the-art algorithms in realistic simulations: ESPEA, NSGA-II, NSGA-III, and SPEA2. The results show that ESPEA and NSGA-II significantly outperform the other two algorithms in our scenario.

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Section: Categorization Of Devices In Buildingsmentioning

confidence: 99%

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

Braun

Mauser

Schmeck

2016

Applications of Evolutionary Computation

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“…The main idea of this transformation is the same for logical constraint transformation and it can be found in [12]. Once this step is completed, the pivot model contains equality and inequality constraints.…”

Section: Fig 4: Time Discretization Patternmentioning

confidence: 99%

“…This schema shows how the linearization process can be automatized using different patterns that were presented in [12,42]. This process deals with non-linear terms as follows:…”

Section: Fig 5: Linearization Processmentioning

confidence: 99%

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Automatic Generation of Model for Building Energy Management

Ngo¹,

Hadjsaïd²,

Maulik³

2016

ijacsa

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Abstract-This paper proposes a model transformation approach for model-based energy management in buildings. Indeed, energy management is a large area that covers a wide range of applications such as simulation, mixed integer linear programming optimization, simulated annealing optimization, model parameter estimation, diagnostic analysis,. . . Each application requires a model but in a specific formalism with specific additional information. Up to now, application models are rewritten for each application. In building energy management, because the optimization problems may be dynamically generated, model transformation should be done dynamically, depending on the problem to solve. For this purpose, a model driven engineering approach combined with the use of a computer algebra system is proposed. This paper presents the core specifications of the transformation of a so-called high level pivot model into application specific models. As an example, transformations of a pivot model into both an acausal linear model for mixed integer linear programming optimization and a causal non-linear model for simulated annealing optimization are presented. These models are used for energy management of a smart building platform named Monitoring and Habitat Intelligent located at PREDIS/ENSE3 in

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“…Validation of energy management systems can be achieved through studies sociological via surveys or questionnaires. A classification of activities in the habitat was proposed in [11]. It relies on the automation level and the number of activations of a device.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of the behavior of people in an intelligent building by camera

Ahmed

Faouzi

Caelen

2013

International Journal on Smart Sensing and Intelligent Systems

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Abstract-an intelligent building is an environment that contains a number of sensor and camera, which aims to provide information that give the various actions taken by individuals, and their status to be processed by a system of detection and classification of behaviors . This system of detection and classification uses this information as input to provide maximum comfort to people who are in this building with optimal energy consumption, for example if I workout in the room then the system will lower the heating . My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use RGB color histograms INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 6, NO. 4, SEPTEMBER 2013 1317 and textures for LBP represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for the detection and classification of the behavior of people in this intelligent building in order to give maximum comfort with optimized energy consumption. The classification is performed using the classification of k 1 and k = 11 in our case, we built 11 models in the learning phase using different nucleus in order to choose the best models that give the highest classification rate and finally for, the classification phase, to classify the behavior, we compare it to the 11 behaviors, that is to say, we make 11 classification and take the behavior that has the highest classification rate. This work has been carried out within the University Joseph Fourier in Grenoble precisely LIG (Grenoble computer lab) in the team MULTI COM and the University of Oran Algeria USTO. Our contribution in this field is the design and implementation of a robust, and accurate system that make detection and classification of 11 behaviors cameras in an intelligent building, varying illumination it means, whatever lighting is our system must be capable of detecting and classifying behaviors.

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An optimal approach for electrical management problem in dwellings

Cited by 52 publications

References 13 publications

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

Automatic Generation of Model for Building Energy Management

Detection and classification of the behavior of people in an intelligent building by camera

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