Application of GLBP Algorithm in the Prediction of Building Energy Consumption

Lv, Dinghao; Zhong, Bocheng; Luo, Jing

doi:10.14569/ijacsa.2015.060607

Cited by 3 publications

(4 citation statements)

References 6 publications

(6 reference statements)

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“…The purpose of Mba work was to apply the artificial neural network (ANNs) with Levenberg-Marquardt algorithm for an hourly prediction, 24-672 h in advance of (IT) and (IH) in buildings found in hot humid region with results, which testified that ANN can be used for hourly IT and IH prediction [53]. Using the neural network to predict the energy consumption of the building resulted in some shortcomings, which were solved of Dinghao's proposed model (a new algorithm which combined genetic algorithm with the Levenberg-Marquardt algorithm) for qualified of predict short-term energy consumption in buildings accurately and efficiently [54], [55]. Yuce presents an ANN approach to predict energy consumption and thermal comfort level of an indoor swimming pool with ANN (Levenberg-Marquardt algorithm) based prediction approach for a specific HVAC system [56].…”

Section: Second Part-the Optimized Artificial Neural Network Model Wimentioning

confidence: 97%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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Section: Second Part-the Optimized Artificial Neural Network Model Wimentioning

confidence: 97%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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“…From the full variety of project communication issues this study considered links between the structural balance of the project network and the project success rate by the end of the conflict. According to the theory of F. Heider [19], there is structural balance in a social network when in do not contain relations -"positive attitudes (friendship, cooperation) between A and B and between B and C, but negative attitudes (hostility, rivalry) between B and C". It is supposed that balanced networks are more comfortable for participants and more stable than unbalanced.…”

Section: Methodsmentioning

confidence: 99%

“…14 participants. The main object: development of the online educational games [19]. The conflict subject: disagreements about the final versions of the game concept and scenarios.…”

Section: Methodsmentioning

confidence: 99%

Using social network analysis techniques to study the efficiency of interproject communication

Plokhov

Osipov

Bubnov

2016

ITM Web of Conferences

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Traditional performance monitoring techniques in project management are based on classical measurescosts, schedule and scope of work. But focusing our view on these indices and their derivatives we can overlook one of the most important elements of project management-project participants. Degrees of their coordination, cohesion and collaboration directly influence on the extent to which project goals could be achieved. Communications and relations between participants play a vital role of special glue holding project parts together. Besides, communication environment integrate within itself information about all aspects of a project -successes, failures and conflicts. Character of interpersonal relations and structure of communications between participants are proposed as indicators of successfulness or failures. In present report project participants are considered as a social network. Using social network analysis (SNA) techniques provides opportunities to examine links between structural characteristics of interproject communication networks and project performance.

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“…To resolve these problems, it is necessary to analyze the IoT users' usage data, and an intelligent manager is required as a platform that comprehensively controls and manages this analyzed usage data. Here, the term intelligent manager does not refer to an IoT platform that simply connects IoT devices, but rather a manager that provides services, which create a customized space for the user in line with the purpose of a smart home, and network technology to minimize energy usage [27,28].…”

mentioning

confidence: 99%