Internet of Things for Power and Energy Systems Applications in Buildings: An Overview
Bibliographic record
Abstract
This paper highlights state-of-the-art technologies, collectively described as Internet of Things (IoT) to improve energy efficiency in residential and commercial buildings. An appraisal of numerous studies on the subject was carried out to better understand the methods through which IoT systems are being currently deployed to improve energy efficiency in buildings, and further technological enhancements that are needed to accelerate these efficiency gains. Many of the present IoT studies have been focused on one specific problem, inefficient heating systems in buildings. Core IoT elements, such as the hardware components and management control software are usually discussed to some degree in most of the research studies. Although the types of applications varied per study, similar design patterns were observed. Some of the major design commonalities are: the choice of options for powering sensors and actuators; control methods for collecting data and controlling devices; historical data acquisition as a base to forecast ahead energy usage; and the methods by which IoT elements interact with one another. The research indicates that while similarities exist among different IoT systems, any chosen system design must be largely specific to its application. Factors such as the number of sensors used, the number of actuated devices, the optimal control method, and personal security of the end-user are application-dependent and must all be considered. Future research directions should examine optimal methods of providing remote power to devices, decipher strategies to keep isolated IoT networks secure, maintain a balance between meeting user comfort requirements and allowing a system to remain relatively autonomous, and optimize system performance in the context of the above requirements and constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".