Energy Demand Forecast in China Based on Particle Swarm Optimization Algorithm
Bibliographic record
Abstract
Energy demand forecast is fundamental to energy planning and policies formulation.There are many factors such as economic growth,population,industrial structure,proportion of urban population,energy consumption structure and technological progress that can affect energy demand.Through particle swarm optimization algorithm,two forms of equations(linear and exponential) are proposed to establish energy demand forecast model based on affecting factors.The training sample between 1980 and 2005 is applied to estimate the coefficients of the model,and the testing sample between 2006 and 2010 is used to verify the established model.The results show that the predictive ability of exponential model is slightly better than the linear one,but the predicted values of both are close to actual values with a smaller estimation error: the average relative errors are 0.76% and 0.57% in fitting part,and are 0.78% and 0.624% in predicting part,respectively.This turns out to be effective for particle swarm optimization algorithm to solve nonlinear and high dimensional identification problem of China energy system.Finally,energy demand over the years 2011-2015 are predicted by analyzing the trends of affecting factors.Based on the growth rate of affecting factors set in this paper,China's energy demand will increase from 3 436.685 millions of tons of standard coal(Mtce) to 4 321.695 Mtce in 2015,with an average growth rate of 5.9%,which indicates a grim situation still exists in the 12th Five Year Plan period of China.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".