Bibliographic record
Abstract
The D7.7 Demonstration Report is one of the final deliverables within the PRELUDE project, designed to showcase the efficacy of innovative technologies aimed at optimizing energy use in the built environment. RELUDE seeks to address the EU's ambitious goals of reducing greenhouse gas (GHG) emissions by 50% by 2030 and achieving carbon neutrality by 2050. As buildings account for a significant share of energy consumption and emissions, the project's technologies aim to transform residential energy dynamics by leveraging real-time data, predictive algorithms, and proactive building management solutions. This report consolidates findings from the case-study implementations across Europe, offering a comprehensive evaluation of energy performance, occupant comfort, and cost-effectiveness. Each demonstration employed tailored technologies to reduce energy demand, increase efficiency, and integrate renewable energy solutions. These technologies were assessed in real-world scenarios, comparing simulated projections with actual performance metrics. Key performance indicators (KPIs) such as energy savings, indoor comfort, and economic viability were rigorously analysed. Key Outcomes: 1. Energy Efficiency Improvements: Demonstrations validated the potential of integrated systems like Fiber Optic Sensors (FOS) for temperature monitoring and predictive maintenance, resulting in significant energy optimization. 2. Enhanced Occupant Comfort: Solutions like occupancy modeling and the Free Running module showcased advancements in adaptive comfort strategies, enabling proactive management of indoor conditions based on real-time data. 3. Scalability and Flexibility: Technologies such as the Optimal RES Selector and the Dynamic Energy Forecast Tool demonstrated their adaptability across varying building typologies and climates, underlining their potential for widespread adoption. 4. Predictive and Proactive Capabilities: Innovations in predictive maintenance, weather forecasting, and energy demand prediction highlighted the project's capacity to minimize operational inefficiencies and enhance system reliability. 5. Environmental Impact: Results showed a tangible reduction in GHG emissions and energy costs, aligning with the EU’s sustainability objectives. The findings emphasize the transformative role of cutting-edge technologies in advancing energy efficiency and sustainability in the residential sector. This deliverable not only validates the technological approaches within the PRELUDE project but also serves as a benchmark for future large-scale implementations, fostering a roadmap for achieving carbon neutrality in the built environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.034 |
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".