Local energy monitoring to support smart energy management and decarbonized town development
Bibliographic record
Abstract
Introduction With the increase in variable renewable energy and liberalization of the electricity retail market, the integrated energy management of demand and supply has become crucial, complementing traditional energy-saving efforts. Therefore, high-resolution energy demand-side information, accounting for household attributes and owned equipment, is essential. However, previous studies have identified energy consumption based on surveys of utility bill payments; this approach does not provide detailed, hourly data on electricity demand, which changes over time owing to lifestyle patterns and weather conditions. Therefore, we aimed to investigate the actual electricity consumption in residential buildings in Shinchi Town using the developed electricity monitoring system.Material and Method In support of regional disaster recovery initiatives, we developed a regional information and communication technology (ICT) system as a network interface for residents. As part of a social experiment, network terminals of the local ICT system were distributed to approximately 100 households in the town. Power monitoring data were collected via equipment installed in home distribution boards and centrally managed by a cloud server.Results and Conclusion We analyzed electric power consumption patterns, focusing on household attributes, seasonal variations in hot-water-supply device usage, and differences between weekdays and holidays. The results indicated that the use of electric power to heat water notably contributed to overall consumption. We analyzed seasonal variation and temperature sensitivity of electricity consumption based on electricity monitoring data from houses and clarified characteristics based on household attributes and equipment. This result is important because it indicates a strong potential for balancing supply and demand in local power management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".