Research on the Evaluation of the Efficacy of Comprehensive Agricultural Energy Electricity Substitution in the Environment of Energy Saving and Emission Reduction Based on Fuzzy Clustering Algorithm
Bibliographic record
Abstract
The purpose of this paper is to explore the effectiveness of integrated energy electric energy substitution in agriculture in the environment of energy saving and emission reduction. The fuzzy clustering algorithm is used to divide different family clusters according to the energy saving and emission reduction ability based on the use of comprehensive energy in agricultural enterprises. Based on the exponential smoothing method and equivalent calorific value method, a prediction model of electric energy substitution efficiency was constructed. Combined with the ANP method, the evaluation indexes of agricultural electric energy substitution efficiency were weighted and graded. In this paper, 100 agricultural enterprises are divided into 5 clusters according to their energy saving and emission reduction ability, which are optimization level, managed level, development level, pressure-based level, and initial level. The model of this paper predicts that the consumption of terminal coal, oil, and electric energy of agricultural projects in 2027 is between 31663.68 and 7447.991.67 million tons of standard coal, and that the electric energy substitution in the same year can be up to 693,755.69 million tons of standard coal. The comprehensive scores of the first-level indicators of economic benefit, environmental benefit, and social benefit are 91.06, 91.26, and 92.01 in order, and the comprehensive efficiency grade is “Excellence”. To summarize the results, this study suggests increasing the investment of electric energy substitution equipment in agricultural production to promote the synergistic development of integrated energy system and electric energy substitution strategy.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".