GRU-Based Load Forecasting for Microgrid Systems: Modeling and Simulation Using Simulink
Bibliographic record
Abstract
This paper investigates the use of short-term forecasts for microgrid operation under uncertainties of the renewable generation. Using hourly data from Ontario's K0K region in 2018, three representative months, January, July and December, were chosen to reflect seasonal conditions. We trained different univariate GRU models for load, photovoltaic and wind generation with the input window of 24 hours. We tested the predictions in a dual-branch MATLAB/Simulink setup, where one branch ran on actual profiles a nd t he o ther o n GRUbased forecasts, both controlled by the same BESS system. System performance was analyzed through grid export, battery discharge, and state of charge (SoC) and provided in the form of key performance indicators (KPIs). The GRU models provided reasonable statistical fits (R² of 0.78-0.96 for all sources and seasons). More importantly, stability-reflecting K PIs l ike g rid peak demand and average SoC were largely unaffected by forecastdriven operation, with only minor variations, while energy flowreflecting K PIs s uch a s g rid e xport a nd B ESS t hroughput were more impacted. This shows that GRU-based forecasts can be trusted for hour-ahead microgrid operation, since keeping the system stable matters more than fully correcting forecast errors.
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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) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".