Enhanced Signal Processing-Based Load Forecasting in Smart Grids Using Artificial Neural Networks and Heuristic Optimization
Bibliographic record
Abstract
The incorporation of renewable energy sources into contemporary power systems necessitates accurate management of the demand-supply equilibrium, highlighting the critical role of smart grids.Signal processing techniques play a crucial role in improving load forecasting by refining feature extraction, noise reduction, and classification.This study introduces an innovative signal processing-driven approach for intelligent load forecasting in smart grids, utilizing Artificial Neural Networks (ANN) optimized by the Smart Flower Water Wave Optimization (SFWWO) algorithm.The SFWWO combines Water Wave Optimization (WWO) and Smart Flower Optimization Algorithm (SFOA) to enhance forecasting accuracy and reliability.Additionally, key signal processing techniques, such as feature selection using Motyka and Ruzicka metrics and fusion via Dice Similarity, ensure improved data preprocessing and classification.The ANN_SFWWO model outperforms existing methods, achieving the lowest RMSE (0.225), MSE (0.040), and MAPE (0.770) on the ERCOT Load Data.These findings highlight substantial improvements in energy efficiency, noise-resistant forecasting, and grid stability, underscoring the role of advanced signal processing techniques in optimizing smart grid operations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".