Enhancing fairness and efficiency in community energy systems: A forecast-driven approach
Bibliographic record
Abstract
The integration of renewable energy sources and community energy storage systems offers a transformative solution for enhancing grid resilience and reducing reliance on fossil fuels. However, ensuring fair and efficient energy allocation among prosumers—individuals who both produce and consume energy—remains challenging, particularly in communities with diverse behavioral patterns. This article addresses these challenges by proposing a mechanism that facilitates equitable energy sharing. It combines long short-term memory (LSTM) forecasts of prosumer energy demand and generation profiles with a relative contribution-based mechanism to manage of a shared battery energy storage system. This mechanism dynamically allocates energy based on a prosumer’s contribution index, which reflects their energy input into the system relative to their demand. A proportional acceptance factor is also introduced to ensure equitable participation in energy allocation, especially when storage capacity is limited. Experimental results demonstrate that integration of forecasting significantly enhances prosumer contributions, reduces disparities in energy access, and optimizes battery charging and discharging cycles. The integration of LSTM forecasting improves the overall fairness and efficiency of the energy management system. This study shows that the proposed mechanism fosters optimal collaboration among prosumers, particularly during high-demand periods or supply shortages. By aligning energy access with individual contributions and accounting for the dynamic nature of prosumers’ behavior, the approach supports energy justice and improves the reliability of battery energy storage systems in the community. These findings suggest that such mechanisms could play a pivotal role in future smart grid systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".