Proposal and Implementation of a User-Centered Design Approach for Smart Grid Technologies: Example of the VERTPOM® Project
Bibliographic record
Abstract
Acting to preserve our planet as much as possible is no longer optional in today's world. To do so, Smart Grids within the framework of electrical networks - involving not only Distribution System Operators (DSOs), but also consumers in their Energy Demand Management (EDM) activity - represent an innovative and sustainable solution. However, the integration of Smart Grids into network management or into consumers' homes implies changes at several levels: organizational, social, psychological, etc. This is why it is essential to consider the human factor in the design of the technologies used in these Smart Grids. This paper proposes the integration of DSO operators and consumers within a user-centered evaluation approach in order to design Smart Grids that are sufficiently acceptable to users to enable Positive Energy Territories that produce more energy than they consume. This demonstration will be illustrated by the VERTPOM® project aiming at facilitating the use of renewable energies specific to each territory in order to contribute to the reduction of greenhouse gases and make the territories less dependent on traditional energies, and thus make Picardy (in France) a Positive Energy Territory. This paper presents the user-centered evaluation approach applied to three technologies (i.e., the VERTPOM-BANK® supervision tool intended for DSO operators, the private web portal and the IBox smart meter intended for households) from the upstream design phase to the implementation of the technologies in real-life situations.
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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 | 0.022 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".