Bibliographic record
Abstract
The falling costs of battery storage and photovoltaic systems have substantially increased the number of "solar-plus-battery" installations in homes and buildings. The solar-plus-battery system enables homeowners to protect their homes during a power outage and save on their electricity bills by stacking multiple value streams that battery storage can provide. In this paper, we present EnergyBoost, a system that proactively controls battery charge and discharge operations, and investigate whether it makes sense economically to install a battery controlled by this system in different jurisdictions with distinct tariff structures. EnergyBoost solves an optimal control problem over a finite time horizon relying on physical models of a solar inverter and a lithium-ion battery, and supervised learning models for predicting the next day available solar energy and household demand. We propose two learning-based control algorithms for EnergyBoost, namely model predictive control and advantage actor-critic. We implement these algorithms on a Raspberry Pi and compare their performance with a rule-based controller under various pricing schemes using real traces of solar irradiance and power consumption of 70 homes located in the same jurisdiction. Our results indicate that EnergyBoost'S control policy outperforms the baseline policies in terms of reducing the average monthly electricity bill, yielding a bill that is, on average, only 7.6% worse than the best bill that can be theoretically achieved.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".