A Multi-Objective Optimization Model for a Non-Traditional Energy System in Beijing under Climate Change Conditions
Bibliographic record
Abstract
In recent years, with the increase of annual average temperature and the decrease of annual precipitation in Beijing, the fragility of Beijing’s energy system has become more and more prominent, especially the balance of electricity supply and demand in extreme weather. In the context of unstable supply of new and renewable energies, it is imperative to strengthen the ability of the energy system to adapt to climate change. This study first simulated climate change in Beijing based on regional climate data. At the same time, the Statistical Program for Social Sciences was used to perform multiple linear regression analysis on Beijing’s future power demand and to analyze the impact of climate change on electricity supply in both the RCP4.5 and RCP8.5 (representative concentration pathway 4.5 and 8.5) scenarios. Based on the analysis of the impact of climate change on energy supply, a multi-objective optimization model for new and renewable energy structure adjustment combined with climate change was proposed. The model was then used to predict the optimal power generation of the five energy types under different conditions in 2020. Through comparison of the results, it was found that the development amount and development ratio of various energy forms underwent certain changes. In the case of climate change, the priority development order of new and renewable energies in Beijing was: external electricity > other renewable energy > solar energy > wind energy > biomass energy. The energy structure adjustment program in the context of climate change will contribute to accelerating the development and utilization of new and renewable energies, alleviating the imbalance between power supply and demand and improving energy security.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".