Quantifying spatio-temporal carbon intensity within a city using large-scale smart meter data: Unveiling the impact of behind-the-meter generation
Bibliographic record
Abstract
This study introduces a novel method for calculating spatio-temporal carbon intensity variations within a city using smart meter data. By integrating smart meter data with solar radiation data from weather satellites, the method predicts electricity demand and solar power generation across 1-km grid areas, achieving higher spatial resolution for carbon intensity distribution than existing models. Accounting for behind-the-meter self-consumption enables dynamic visualisation of carbon intensity variations driven by renewable energy adoption in localised urban areas, offering a more detailed assessment compared to conventional methods focusing solely on temporal fluctuations in the grid's energy mix. The method was applied to a dataset of approximately 410,000 smart meters in Utsunomiya City, Japan. Findings reveal that carbon intensity variations are affected by weather and seasonal changes. Notably, suburban areas with a higher proportion of prosumers exhibit lower carbon intensity than urban centres, highlighting significant intra-city variations linked to local renewable energy utilisation. This method can enhance the efficient use of distributed energy resources within cities and support prioritising low-carbon renewable energy through strategies such as demand response program development, optimising electric vehicle charging schedules, and identifying priority areas for photovoltaic and battery storage deployment. • Developed a method to quantify carbon intensity in localised areas within a city. • It considers hourly behind-the-meter PV self-consumption and grid electricity CI. • Applied the method to a Japanese city to visualise real spatio-temporal CI. • Significant intra-city CI differences linked to local renewable energy utilisation. • The method promotes efficient use of DERs and prioritises low-CI renewable energy.
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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 | 0.001 | 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".