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Alternatives to solar_mcp

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables voice assistants to answer when to run household appliances using live solar forecasts, typical-year production, savings, battery, and multi-load planning. It remembers site details, estimates usable solar windows, and returns concise spoken guidance without inventing measurements.
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables portable-battery outage planning by exposing tools that retrieve an example scenario, compute usable AC energy and runtime, select protected and optional loads, and compare plans under energy, continuous-power, and startup-surge limits. It returns structured calculations and spoken summaries for rehearsing what can stay powered before an outage.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables conversational home power management by providing real-time solar, battery, and grid status, plus forecasts to answer questions like whether you can run an appliance. It supports Victron and Growatt inverters, remembers household appliances and grid events, and renders an interactive dashboard card in MCP-compatible clients.
      5 npm
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI-powered analysis and prediction of household energy consumption through machine learning models, providing historical consumption breakdowns, price queries from Spanish electricity markets, and personalized energy optimization recommendations.
      -
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to manage household schedules by checking availability, proposing minimal changes, and requiring explicit human approval before executing any action.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants to size hybrid renewable energy systems by simulating candidate designs hour-by-hour for a full year, filtering out constraint violations, and ranking remaining options by net present cost and related techno-economic metrics.
      27 PyPI
      Apache 2.0