Design and environmental sustainability assessment of energy-independent communities: The case study of a livestock farm in the North of Italy
Bibliographic record
Abstract
This paper investigates the energy and economic performance of several energy schemes that could potentially be applied to agricultural and zootechnical communities contributing to the international objectives of sustainable development. The proposed energy schemes involve integrated energy efficiency technologies and novel system layouts aiming at reaching the zero-energy goal at a community level, by considering collective energy actions with provision of benefits for members and stakeholders. The proposed scenarios include different innovative technologies, such as anaerobic digestion, cogeneration, biogas upgrading, solar, district heating and cooling. These layouts are modelled in TRNSYS simulation environment to perform dynamic simulations and parametric analyses of the pivotal system parameters. Such analyses are conducted to find out the best scenario and the size of its system components which optimize different energy and economic objective functions. To assess the feasibility of all proposed scenarios and energy schemes, as well as to investigate the potential of the developed models, proposed scenarios are studied for an existing community. This existing agricultural community named “La Bellotta”, is served through different technologies, including a gas fuelled co-generator and an anaerobic biodigester. Simulation results show that the investigated scenarios allow for achieving very high self consumption ratios of energy produced on-site (from 57 to 100%), high economic performance (measured by the profitability index up to 1.35 for the best investigated scenario) and environmental benefits. The case study provides examples of energy schemes in which citizens and communities have a major benefit to invest in projects including renewables technologies, energy efficiency, and positive energy services.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".