Building Monitoring

2020 ◽

pp. 60-65

Keyword(s):

Rational Choice ◽

Construction Industry ◽

Correct Choice ◽

Automated System ◽

Digital Code ◽

Automated Control ◽

Sigma Delta ◽

Processor Core ◽

Building Monitoring ◽

High Level

In automated control systems for technical processes, the conversion of a continuous signal into a digital code and vice versa from a digital code to a continuous (analog) value is widely used. For direct type converters often used the term ADC, the reverse - DAC. The characteristics of the converters often dramatically affect the parameters of the entire automated system. The importance of the correct choice of ADCs and DACs has especially increased recently in connection with the mass introduction of microcontrollers MC. Indeed, in addition to the ADC and DAC, it is necessary to place the processor core in the microcontroller's crystal, I/O interfaces and many other elements necessary for the functioning of the MC. The use of information converters in the construction industry imposes additional requirements on converters: for example, in building monitoring systems, precision ADCs with extremely high accuracy are often required (while performance may be low), in other applications it is necessary to provide the necessary parameters at a high level of industrial interference, etc. This article explores issues related to the rational choice of ADCs and DACs, taking into account current trends in the IT field and the specifics of work in the construction industry. Sigma-Delta converters are noted as the most promising models of direct type converters.

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Effective Missing Value Imputation Methods for Building Monitoring Data

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9378230 ◽

2020 ◽

Author(s):

Brian Cho ◽

Teresa Dayrit ◽

Yuan Gao ◽

Zhe Wang ◽

Tianzhen Hong ◽

...

Keyword(s):

Monitoring Data ◽

Missing Value ◽

Imputation Methods ◽

Missing Value Imputation ◽

Building Monitoring

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Proposing 3D Thermal Technology for Heritage Building Energy Monitoring

Remote Sensing ◽

10.3390/rs13081537 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1537

Author(s):

Antonio Adán ◽

Víctor Pérez ◽

José-Luis Vivancos ◽

Carolina Aparicio-Fernández ◽

Samuel A. Prieto

Keyword(s):

Computer Vision ◽

Dimensional Space ◽

Three Dimensional ◽

Building Energy ◽

Energy Monitoring ◽

3D Computer Vision ◽

Building Monitoring ◽

Heritage Buildings ◽

Heritage Building ◽

Definition Of

The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updating of the current building energy monitoring methodologies. This paper provides a detailed definition of the most significant aspects of this new extended methodology and presents a case study showing the potential of 3D-TCV techniques and how they may complement current techniques. The results obtained lead us to believe that 3D computer vision can provide the field of building monitoring with a decisive boost, particularly in the case of heritage buildings.

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Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Sensors ◽

10.3390/s21093299 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3299

Author(s):

Eva Lucas Segarra ◽

Germán Ramos Ruiz ◽

Carlos Fernández Bandera

Keyword(s):

Optimization Technique ◽

Load Forecasting ◽

Weather Forecast ◽

Building Energy ◽

Load Forecast ◽

Energy Saving Potential ◽

Energy Models ◽

Building Monitoring ◽

Probabilistic Load ◽

Grid Operators

Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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