The Design of Building Management Platform Based on Cloud Computing and Low-Cost Devices
Bibliographic record
Abstract
The Design of Building Management Platform Based on Cloud Computing and Low-Cost Devices Li-Te Huang, Yi-Yang Chiu and Ying-Chieh Chan Pages 407-414 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Indoor environment monitor and control are important aspects of reducing building energy consumption and maintaining occupants visual and thermal comforts. Previous research showed that the lots of buildings were not able to provide designed service level in the daily operation and the feedback from occupants showed that the predetermined service level sometimes did not match occupants desire. However, most of existing buildings and residential buildings did not have building management systems that can systematically monitor the indoor environment, collect occupants feedback, and fine-tune built-in control logic when the occupants needs could not be met. An easy-to-build and easy-to-use build management system can help researchers, engineers, and occupants themselves to identify the issue. The goal of this study was to develop a building management platform with the functions of indoor environment data collection (temperature, relative humidity, solar radiation, etc.), remote control (air conditioner, window, shading), occupants feedback collection (set point, status of building components, etc.), and user interface for retrieving the data and modifying control logic. The platform was developed using Arduino-based and Raspberry Pi-based microcontroller board, low-cost sensors, 3D printing technology, and cloud computing technology. The stored data can serve for personal behavior/comfort analysis, components efficiency analysis, and advanced control logic development. This last part of this paper demonstrated the ability of the developed platform and exhibited the potential application. In the end, we provided further discussion about the potential challenges we might face when developing building management systems in other existing spaces/buildings. Keywords: Building Management Platform; Cloud Computing; Low-Cost Devices DOI: https://doi.org/10.22260/ISARC2019/0055 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".