Applying Energy Justice Principles to Renewable Energy Trading and Allocation in Multi-Unit Buildings
Bibliographic record
Abstract
Although rooftop PV panels and battery energy storage systems have been well established for detached residential buildings, there is still a lack of access to the advantages of onsite renewable energy generation and consumption for residents of multi-unit buildings. To understand the effects of developing distributed renewable energy sources for multi-unit buildings, a new fair energy-sharing model in which different groups of residents can gain benefit from the shared energy systems is proposed. Despite the potential benefits of developing renewable technologies in multi-unit buildings, the energy trading and allocation processes in the buildings can be unfair for some groups of residents. Accordingly, this work studies the main principles of energy justice and analyses how these principles can be applied in the energy trading and allocation processes to achieve fair energy sharing. In addition to fairness and justice, the experimental results show that our method increases the sellers’ profit by 59.7–127% and decreases the buyers’ cost by 8–21%, compared to the baseline methods. Moreover, applying the energy justice principles in the proposed sharing models acts as an efficient incentive for the residents of the multi-unit buildings to invest in the shared distributed renewable energy sources.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".