Energy consumption analysis of residential swimming pools for peak load shaving
Bibliographic record
Abstract
Peak load shaving is a very important issue, however, most of peak load shaving methods either require extra investments or reduce the comfort of the consumers. This paper analyzes the impact of circulating pumps of residential swimming pools on the peak load, and shows that shifting the active time of circulating pumps of residential swimming pools could shave the peak load without requiring extra investments or reducing the comfort of the consumers. First, based on an extensive dataset containing hourly energy consumption readings of 1005 residents during March 2011 and October 2012 in South Ontario, this paper analyzes the features of the energy consumptions of residents with and without swimming pools. Second, this paper proposes a novel non-intrusive appliance load monitoring method to estimate the energy consumption of circulating pumps of residential swimming pools. The advantages of the proposed method are that, compared with other non-intrusive appliance load monitoring methods, it does not require high sampling rate data or prior information of the appliance, therefore the cost of implementation is reduced and users’ privacy is protected. Finally, this paper shows that, the average hourly energy consumption of CPRSP is 0.7429 kW h. During the peak hour, circulating pumps of residential swimming pools contributes 20.11% energy consumption of residents with swimming pools, as well as 8.79% peak load of all neighborhoods. When considering minimizing the peak load, by postponing circulating pumps of residential swimming pools for 8 h rather than turn them off, the peak load can be shaved by 4.64%. When considering minimizing the cost of circulating pumps of residential swimming pools, the peak load can be shaved by 3.15% by postponing circulating pumps of residential swimming pools for 6 h, meanwhile the peak hour is postponed from 18:00 to 19:00.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".