Examining the potential of Advanced Electricity Metering in New Zealand
Bibliographic record
Abstract
This dissertation examines advanced metering technology and its application to the context of domestic electricity use within New Zealand. It identifies the current state of advanced metering technology and the associated effects of its introduction within an international context. This is achieved through a review of academic literature and contextualised through an assessment of current markets, products, and cases of large scale advanced metering deployments. This information is required in order to ascertain the spread of options existing currently and provide data, which is crucial for the design-led modelling and scenario building process. After a close examination of the design options, and theoretical models for advanced metering, an improved model is generated so the available configuration of products within various types of systems can be understood and communicated. Information from the investigation of New Zealand's domestic environment, with a specific emphasis of energy-use, is provided to create a context for the use of advanced metering systems. This research develops an innovative tool, which allows the complexity, and variability of advanced metering systems to be communicated, modified, and analysed depending on a specific context. By applying this tool a range of scenarios are generated that demonstrate potential options for the New Zealand context. Taken together they encompass the spread of viable advanced metering options, and clearly demonstrate the still largely untapped potential of advanced metering technology in the New Zealand context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".