Breaking Down Prune Low-value Tuning And Diagnostic

Over the past months, solar optimization tools have added layers of tuning knobs and diagnostics - many meant to improve clarity, but often adding invisible friction. Some settings, like the target shortfall penalty slider in self_consumption mode, obscure critical decisions instead of clarifying them. What’s baked in rarely moves the needle, yet clutters the planner’s logic. nnHere’s the core: the CONF_TARGET_PENALTY knob feels impactful but often gets bypassed, while hybrid_solar_accuracy and the ForecastAccuracyComparisonSensor linger as data clutter - never tied to action. Even worse, types.py still defaults target_shortfall_penalty_per_pct to 0.030, even though the UI and runtime use 0.015, creating a disconnect between what’s documented and what’s real. nnPsychologically, this reflects a broader trend: more options don’t mean better control. Users chase precision in settings that don’t shape outcomes - like tuning a knob that the system ignores. The emotional toll? Frustration from tuning without progress, and debugging confusion from invisible defaults. nnHere’s the elephant in the room: those “hidden” knobs aren’t just noise - they’re distractions. They slow down troubleshooting, confuse new users, and breed mistrust in the system’s intelligence. If your battery’s not behaving as expected, the real problem may not be the data, but the unnecessary noise in the code. nnFix it by simplifying: remove or internalize the ignored penalty slider, scrap unused metrics, and align defaults across layers. Let users focus on what matters - target SOC levels, clear forecasts, and real performance - not ghost knobs that vanish in the plan.”