Robust model predictive control of battery energy storage with neural network forecasting for peak shaving in university campus
Bibliographic record
Abstract
This study addresses the challenge of optimizing energy consumption and managing peak demand charges in large university campuses using battery energy storage system (BESS) by demonstrating the effectiveness of a two-stage neural network-based Model Predictive Control (MPC) algorithm enhanced with robust optimization. To achieve this, we first delineate the architecture of neural networks and the Robust MPC model. Subsequent testing in simulation environments leads to the practical validation of the algorithm on a small-scale test bench configured to emulate a microgrid system. Results show that the integration of neural networks and robust optimization in an MPC framework significantly outperforms traditional control methods, achieving more effective peak shaving, reducing energy costs, and enhancing system resilience. The added robustness effectively addresses forecasting errors, making the control strategy more resilient and reliable. The successful deployment of this algorithm on a test bench underscores its practical applicability, highlighting its potential to optimize energy consumption and reduce peak demand charges in buildings. This research contributes a novel, scalable, and adaptive control strategy that bridges advanced forecasting techniques with robust MPC, providing a valuable solution to address peak demand challenges in commercial and institutional buildings. • A robust neural network-based model predictive control system is proposed. • The system integrates robust optimization with a tunable budget of uncertainty. • The system achieves efficient peak shaving in large buildings using batteries. • Validation is performed on a small-scale test bench emulating a microgrid system. • The study demonstrates the real-world applicability of the proposed control system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".