Application of Internet of Things in Residential Distribution Systems
Bibliographic record
Abstract
Enabling an internet of things (IoT) application in residential distribution systems by integrating houses with IoT windows and occupant behavior can provide numerous advantages to the power grid, including, but not limited to, demand diminution, congestion reduction, and capacity deferral. This paper presents a new framework that mathematically enables an IoT application in residential distribution systems by integrating IoT windows and occupant behavior with houses for load management and energy conservation. With the proposed framework, we model residential loads considering the IoT concept, and then develop a mathematical optimization model that facilitates the integration of IoT-based houses into the residential distribution system. Different case studies considering a 33-bus distribution network are presented and discussed to demonstrate the effectiveness of penetrating IoT-based houses on distribution system operations and household profitability. It is observed that the profit of the local distribution company decreases when houses are transformed to IoT-based houses due to the fact that less energy is sold to the households. On the other hand, the operation cost of the IoT-based house is lower than that of the conventional house because of the better-managed house energy use, thereby resulting in saving money. It is found that 10% and 20% penetrations of IoT-based houses help reduce the maximum power imported through the distribution substation by 30 kW and 60 kW, respectively. It is also found that the load of IoT-based houses and power availability of a rooftop photovoltaic generation are not compatible, and hence, without an action from the customer and/or utility to coordinate them through a demand response program, IoT-based houses would not contribute to increasing the connectivity of PV-distributed generation in the smart grid.
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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".