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The traditional procedure for the building change detection is subjective to user's knowledge of the involved data. The complexity increases further due to unavailability of a public data set that is scanned on two different dates. Therefore, the manual changes in the reference data are more common to generate modified data. This paper first presents a strategy to introduce five types of changes in...
Distributed model predictive control (DMPC) for thermal regulation in multi-zone buildings continues to gain attention over centralized approaches. Particularly, centralized control approaches have been shown to become impractical when applied to large-scale buildings due to for example, computation complexity, modeling complexity of large buildings and availability of required sensor and actuator...
The paper provides the mathematical model describing the movement of different kinds of avalanche snow mass and its interaction with obstacles based on the modified method of particle dynamics. Further, the authors introduce their algorithm to calculate avalanche impact on buildings and structures; this algorithm underlies a computer program that allows you to set the basic parameters of a building,...
The statistical models required the large data in the time series forecasting. While, to forecast the limited data or small data cannot be suggested by using these models. In this paper, we are interested to apply fuzzy random auto-regression model to handle the university enrollment data. The accuracy of the forecasting model can be improved through the left-right procedure. The yearly enrollment...
Many models have been suggested to predict the weather and temperature data. Most of them used the single point data in building prediction equations. Besides that, the randomness, the vagueness and possibility of the temperature data are also not much concerned. In this paper, we introduce the minimum-maximum procedure for daily temperature modeling based on fuzzy random auto-regression time series...
Modern predictive modeling techniques are commonly used for modeling a target of interest based on a list of input variables. In general, these techniques are capable of identifying input variables associated with the target, but not for the purpose of identifying the causation relationship between target and inputs due to the fact that the data are observational data. Advanced technology has made...
Commercial buildings are responsible for a large fraction of energy consumption in developed countries, and therefore are targets of energy efficiency programs. Motivated by the large inherent thermal inertia of buildings, the power consumption can be flexibly scheduled without compromising occupant comfort. This temporal flexibility offers opportunities for energy savings and the provision of frequency...
Based on core problems of personalized recommendation, traditional collaborative filtering recommendation algorithm and theories of AprioriAll algorithm based on association rule, it is proposed to build two-dimension user interest model combining user's implicit and explicit interests and increase the threshold value of third dimension time in this paper t o realize the real-time personalized recommendation...
Real-time monitoring has become a critical part of distribution network operation that enhances the control and automation capabilities as metering technologies evolve. The metering infrastructure has further extended from feeder head of a substation throughout the entire feeder loads. Despite installations of “smart” meters and related recording devices are increasing rapidly, the measurable area...
Building 3D tunnel model is one of the important work in underground mining management, the methods to build 3D tunnel model by secondary development of commercial software or using OpenGL are not convenient and efficient. After analyzing the structure of DXF file, a method that building 3D tunnel model based on DXF file is proposed. On the basis of designing the tunnel section type and parameters,...
Power Quality (PQ) problems on distribution system (DS) can be categorized as voltage sag, voltage swell, harmonic, transient and others. Nowadays, due to the nonlinear loads, imbalanced loads, improper wiring and poor grounding on DS, the neutral to earth voltage (NTEV) become issue of the PQ problem. Generally, magnitude of the NTEV is zero value if the system is balance, however it changes to the...
Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. In this paper, we study how to build Bayesian networks from publicly released GWAS statistics to explicitly reveal the conditional dependency between single-nucleotide polymorphisms (SNPs) and traits. The key challenge in building a Bayesian network is the specification of...
This paper presents modeling methodologies for predicting energy consumption using system identification. The models discussed will predict the systems performance using the measured input and output. To test and train the models, data was gathered from an existing building. State space, nonlinear, and polynomials models based mathematical functions and tested with different parameters such are temperature,...
This work investigates from a techno-economic perspective the potential for groups of smart buildings distributed throughout an area to provide Demand Side Response (DSR) as a means to increase electricity distribution network capacity. More specifically, intelligent multi-energy flows between buildings within a smart district are optimized with the aim of reducing energy costs for end-users and network...
At present, bridge maintenance management typically consists of regular visual building inspections. Structural damage frequently remains undiscovered until it becomes clearly visible, a situation which makes little economic sense. However, it is often the case that damage and critical reactions to a bridges internal structure occur in inaccessible and concealed places, and are caused by existing...
This work studies how to apply support vector machines in order to forecast the energy consumption of buildings. Usually, support vector regression is implemented using the sequential minimal optimisation algorithm. In this work, an alternative version of that algorithm is used to reduce the execution time. Several experiments were carried out taking into account data measured during one year. The...
Poorly designed excitation signals could lead to inaccurate or, even worse, highly correlated parameter estimates in a data-driven model, so it is critical to have an informative training data set in order to obtain an accurate model in a cost effective manner. This paper investigates a sequential optimal design of experiments (DOE) approach to generate an optimal training data set for varying zone...
In this paper, a procedure to model buildings and their thermal zones is studied. Since, ideally, the developed methodology should be applicable to different buildings, at different scales and in different locations, a feasibility study is necessary. The experiments used to test the flexibility of the model structure and the adaptability of the modeling procedure itself are presented. The obtained...
This work presents a data-intensive solution topredict heating and hot water consumption. The ability topredict locally those flexible sources considering meteorologicaluncertainty can play a key role in the management of microgrid. A microgrid is a building block of future smart grid, it canbe defined as a network of low voltage power generating units, storage devices and loads. The main novelties...
Ambient intelligence (AmI) is a discipline that makes the environments we visit everyday sensitive to us by adding sensors to measure our surroundings and actuators to interact with both the environment and the people in it. But the problem emerges when trying to convert a space into an ambient intelligence environment or to modify one that already exists. This is where a simulator could help to arrive...
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