Comprehensive Energy Retrofit of a 1950s Office Building in Algeria: Toward 2030 Efficiency Goals in Mediterranean Climates
Bibliographic record
Abstract
Retrofitting conventional buildings is a key strategy for climate change mitigation, as it enhances indoor comfort while reducing energy consumption in countries where old building stocks are a major contributor to energy use and associated emissions. In North African and Mediterranean contexts, deep energy retrofits for mid-century office buildings remain limited, particularly regarding the practical implementation of solutions adapted to local climatic and economic conditions. This study investigates a deep retrofit of a mid-20th-century office building in Algeria, aiming to assess its alignment with Algeria’s 2030 climate and energy efficiency objectives. A holistic methodology combining an energy audit and dynamic simulation with EnergyPlus has been undertaken to evaluate envelope upgrading, HVAC replacement, and renewable energy supplementation. Retrofit strategies selected were defined as representative of technically feasible and cost-effective solutions for Algerian mid-century office buildings, balancing energy performance improvement and economic viability under local climatic constraints. This study analyzed two retrofit scenarios: one with a combination of envelope improvements, heat pump replacement, and supplementation by photovoltaic solar panels, reaching 41% in terms of electricity savings (≈23 t CO2/year avoided), and the other with the VRF system, reaching 54% in savings (≈30 t CO2/year). Consequently, energy intensity is reduced from the base case by around 41–54%. The study contributes to data-driven retrofitting studies and explores innovative, low-cost strategies that respond to regional climatic challenges.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".