Integrating Small Scale Green Energy into Smart Grids: Prediction for Peak Load Reduction
Bibliographic record
Abstract
The emerging Smart Grid technologies allow for the integration of clean energy from small-scale energy generators (SEGs). In this paper, we investigate a model by which an electric grid operator (EGOs) schedules the integration of clean energy from residential homes acting as SEGs. These SEGs are equipped with rooftop photovoltaic (PV) and a bank of utility grade battery systems. The challenge facing the electric grid operator (EGO) is that home-based battery systems require several hours of sunlight to charge from rooftop PV panels, and an average 90 minutes to be discharged to 30% original capacity. The EGO must be able to schedule the discharging cycle so that it coincides with the time of the highest peak load during the day for efficient cost reduction. In this paper, we propose a model that allows the EGO to predict the highest peak of energy consumption on the distribution feed where the SEGs are connected. For the realization of the system, we have acquired a dataset which includes time series of energy consumption data for approximately 1500 houses including 3 SEGs. We performed our prediction using the multivariate autoregressive integrated moving average (MARIMA) method and achieved 92.64% accuracy. The real-life implementation of the system and the prediction model are described in this paper.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".