A review on optimization of district energy systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The scientific state-of-the-art indicates that solutions for integrating renewable energy in the energy sector have primarily been sought within the limits of individual energy sub-sectors, focusing on concepts such as 'Smart Grid', 'Zero Energy Buildings', and 'Power-to-Heat', while the heating and cooling sectors have largely been overlooked so far. The heating and cooling sector should undergo a transformation in response to sustainability concerns and greenhouse gas emissions. District energy systems (DES) are expected to play an essential role in the development of climate-neutral societies. However, due to its large scale and its potential integration to a number of other energy systems, DES introduces complexity in design and operation, necessitating optimization studies to achieve key objectives such as reducing operational and infrastructure costs, minimizing emissions, and enhancing efficiency. This review addresses a gap in current research on DES optimization by exploring the technical aspects behind DES optimization and their practical applications. The review begins by outlining the state-of-the-art of DES and their evolution. Following this, the review examines the technical foundations of DES optimization studies in the literature. Moreover, the review outlines the critical components of DES optimization, including problem formulation and algorithms used to find efficient solutions. Additionally, key areas for future research and development in DES optimization are identified.
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.
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) | 0.001 | 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 it