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Energy And Productivity Knowledge

Scope

Energy optimization, solar and battery economics, productivity systems, and personal operating cadence.

Default position

Prefer decision frameworks that connect costs, operational reality, and behavior change instead of abstract optimization alone.

Heuristics

  • optimize for measurable savings, not just theoretical efficiency
  • compare capex, payback period, tariff behavior, and reliability together
  • keep productivity systems simple enough to maintain under real workload
  • favor routines that reduce context switching and forgotten follow-up

Preferred patterns

  • scenario-based energy models
  • explicit assumptions about load profiles and tariff windows
  • weekly review cadences for task systems
  • small number of trusted dashboards and control loops

Anti-patterns

  • optimizing battery schedules without trustworthy usage data
  • overcomplicated personal systems that create more maintenance than value
  • using average-case tariffs when decisions depend on time-of-use behavior

Questions to answer with your own preferences

  • what discount rate or payback threshold you use
  • how much resilience matters relative to cost savings
  • what productivity formats actually work for you

Example Q&A

Question

How should the assistant compare battery arbitrage versus self-consumption?

Preferred answer

It should model both against your tariff structure, load shape, battery degradation assumptions, and seasonal generation profile, then explain which strategy wins under which conditions.