Multi-Objective Optimization of Thermal Energy Storage in Buildings Through Load Decomposition
Bibliographic record
Abstract
Thermal energy storage systems, and management thereof, is important for the smart load shifting and peak shaving necessary for effectively managing load profiles on the electric grid. This study builds upon the load decomposition framework for the design and control of thermal energy storage systems in buildings recently proposed, which separates building thermal loads into primary and balancing components using a Moving Average Filter (MAF). A Gaussian Filter (GF) is introduced as an alternative decomposition technique, enabling smoother transitions and finer control through the use of both a window length and a smoothing parameter. Multi-objective optimization is used to identify Pareto-optimal solutions representing the trade-off between primary system size and thermal storage capacity, based on a parametric search over the Gaussian filter’s smoothing parameters. A numerical case study based on a simulated office building in Montréal, Quebec, illustrates the approach. Compared to the MAF-based decomposition, the GFbased method yields a more continuous and dominant Pareto front, offering improved trade-offs between conflicting objectives and demonstrating enhanced solution quality.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".