Evaluation of meta-heuristic optimization methods for home energy management applications
Bibliographic record
Abstract
Home energy management (HEM) requires optimization techniques to solve multi-variable and multi-objective problems. The optimal use of energy, the occupants comfort, the reduction of peak power and energy cost are objectives with dissimilar variables behaviors. Their solutions increase in complexity with the number of variables which would be a challenge if the real-time response is needed. Meta-heuristics optimization techniques offer great potential for the solution of such complex optimization problems, however, their main inconvenient is that a non negligible number of iterations must be executed which is reflected in a heavy computation loops and high resources utilization. In this paper, three meta-heuristic optimization algorithms are studied and evaluated focusing on HEM applications. As the better feasible option among them, Particle Swarm Optimization (PSO) method have been selected and applied to the comfort control in the residential environment. To achieve the real-time execution of the computational burden of the MPC-PSO implementation, the advantage of VLSI parallelism is used. The FPGA in the loop co-simulation results, using real data of an occupied house, demonstrate the potential of the implemented algorithm and future multi-objective target.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".