Multi-objective Optimization of Digital Management for Renewable Energies in Smart Cities
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
Smart cities mark the shift from the traditional model of urban construction to the planned construction of a composite system between smartness and energy. Considering the defects of renewable energies, e.g., intermittency, randomness, and low dispatchability, it is imperative to explore the digital and unified management of urban renewable energies. Therefore, this paper presents a multi-objective optimization algorithm for digital management, which can quantify the multiple energy models of smart cities. Firstly, the dimensions of renewable energy construction in smart cities were detailed, and the functions, hierarchy, and data flows of the digital management system for renewable energies were plotted in turn. After that, the output probability models of typical renewable energy power generation systems were established, plus the objective functions of digital management for renewable energies. Finally, particle swarm optimization (PSO) was combined with genetic algorithm (GA) for multi-objective optimization of the digital management for renewable energies in smart cities. The proposed models and algorithm were proved effective through experiments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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