Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
Bibliographic record
Abstract
To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper innovatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos optimization algorithm (COA) for short-term load prediction of IES. The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in conventional WNN models. First, the Pearson correlation coefficient is employed to select the key influencing factors of load prediction. Then, the traditional particle swarm optimization (PSO) is improved by the dynamic particle inertia weight. To jump out of the local optimum, the COA is employed to search for personal optimal particles in IPSO. In the iteration, the parameters of WNN are continually optimized by IPSO-COA. Meanwhile, the feedback link is added to the proposed model, where the output error is adopted to modify the prediction results. Finally, the proposed model is employed for load prediction. The experimental simulation verifies that the proposed model has a significant improvement in prediction accuracy and operating efficiency compared with artificial neural network (ANN), WNN, and PSO-WNN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".