How much of an optimal home-battery controller's gain survives a realistic forecast?

What it found

Re-runs the rolling 36 h linear program of energy-tou-lp with forecasts built only from hours already observed (yesterday's weather, or the mean of the last seven days) and executes the plan on the actual PV and load, over 10 seeded synthetic weather years and the same assumed tariff. For 10 kWh behind 6 kW PV the perfect-forecast optimiser saves $892 a year against the best fixed rule's $694; with yesterday's weather it saves $726 and with a seven-day mean $691, so 84–101% of the optimiser's gain over the rule is lost. Life-limited break-even is $667 and $663/kWh against the rule's $674 and the perfect $733. Forecasts of the truth with 10% and 25% multiplicative noise keep 75% and 39% of the gain; at 100% noise the optimiser is worse than the rule. Stylised: assumed tariff, synthetic weather and load, forecast methods naive, noise abstract, crude cycle-count life.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.