Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.13501 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912279518248960 |
|---|---|
| author | Vasques, Xavier Possompes, Thibaut Rey, Herve Touze, Marine Le Auboin, Nicolas Passot, Emmanuelle Lange, Benoit |
| author_facet | Vasques, Xavier Possompes, Thibaut Rey, Herve Touze, Marine Le Auboin, Nicolas Passot, Emmanuelle Lange, Benoit |
| contents | Increases in energy prices and the global goal of mitigating CO2 emissions necessitate the development of intelligent Building Management Systems (BMS) that operate on an energy-efficient basis. Data Centers, buildings and/or group of buildings are often responsible for huge energy consumption. One way to monitor and optimize energy consumption is to instrument buildings using sensors (temperature, pressure, humidity ...) in order to track and solve wrong usage of energy management systems. The majority of the BMS are processing the data dynamically without taking into account the data history due to their constraint problems (time, bandwidth and calculation capability) and data resources. The RIDER project brings together a consortium of research laboratories and enterprises including IBM, to share their expertise in research and development of smart Information Technology (IT) energy platforms. In this context, we aim to improve energy efficiency of buildings or group of building (including data centers) using IT. One of the objectives is to identify valid, potentially useful, and ultimately understandable patterns in data for improving energy efficiency. We propose in this paper an approach of using an integrated platform able to interconnect instrumented buildings and sites, and to provide a high-level point of view for increasing our knowledge from sensors. The expected results are to estimate physical parameters that influence energy consumption based on data set history. Different correlation could be found between different variables, for example, indoor air quality and energy consumption. These results could be applied at a location where no sensor is placed and predict energy consumption from different variables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysis and Knowledge Discovery from Sensors Data to Improve Energy Efficiency Vasques, Xavier Possompes, Thibaut Rey, Herve Touze, Marine Le Auboin, Nicolas Passot, Emmanuelle Lange, Benoit Signal Processing Increases in energy prices and the global goal of mitigating CO2 emissions necessitate the development of intelligent Building Management Systems (BMS) that operate on an energy-efficient basis. Data Centers, buildings and/or group of buildings are often responsible for huge energy consumption. One way to monitor and optimize energy consumption is to instrument buildings using sensors (temperature, pressure, humidity ...) in order to track and solve wrong usage of energy management systems. The majority of the BMS are processing the data dynamically without taking into account the data history due to their constraint problems (time, bandwidth and calculation capability) and data resources. The RIDER project brings together a consortium of research laboratories and enterprises including IBM, to share their expertise in research and development of smart Information Technology (IT) energy platforms. In this context, we aim to improve energy efficiency of buildings or group of building (including data centers) using IT. One of the objectives is to identify valid, potentially useful, and ultimately understandable patterns in data for improving energy efficiency. We propose in this paper an approach of using an integrated platform able to interconnect instrumented buildings and sites, and to provide a high-level point of view for increasing our knowledge from sensors. The expected results are to estimate physical parameters that influence energy consumption based on data set history. Different correlation could be found between different variables, for example, indoor air quality and energy consumption. These results could be applied at a location where no sensor is placed and predict energy consumption from different variables. |
| title | Analysis and Knowledge Discovery from Sensors Data to Improve Energy Efficiency |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.13501 |