Digital Platforms for Renewable Energy Communities Projects: An Overview
Bibliographic record
Abstract
The European Union energy policy agenda of achieving the transition to carbon neutrality has been established by an important legislative package called "Clean Energy for all Europeans". A novel approach introduced was to put the citizen at the center of the energy transition. On one side, by powering his freedom of action and, on the other side, by asking him an exceptional engagement in energy consumption reduction activities and in participating in the investments for new distributed Renewable Energy Sources (RES) power plants. The Renewable Energy Communities (REC) is the policy framework used to implement this strategy introduced by the Renewable Energy Directive Recast (RED II). In particular, RECs promote citizen’s active role by encouraging energy consumption reduction and energy demand flexibility while reducing the Not In My Bachyard (NIMBY) effect towards RES. Each member state is transposing the RED II directive, adapting it to national legislation and energy transition strategy. Pioneers countries like Italy have already started the experimentation of this framework and developing the first pilot projects. The citizens’ interest and their will to participate in REC projects indicate the need for supporting tools guiding them along all the project development stages: “design”, “creation”, and “operation”. This work presents three categories of supporting digital tools and platforms required to develop REC projects: Commercial, EU Founded and Freeware. We analyzed 30 tools, evaluating the services provided in each of the different stages of REC project implementation.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".