Open Arsenal

Precision for real-world power.

Wattonomy is an open-source energy decision intelligence platform designed to help users understand the true cost of heating, cooling, and utility systems based on real-world conditions. Instead of relying on generic estimates or marketing claims, it builds detailed financial and energy models that account for location, climate, utility rates, system efficiency, installation costs, and long-term maintenance. The goal is to give users a clear, grounded view of what different systems actually cost over their full lifecycle.

At its core, Wattonomy simulates how different energy systems perform in a specific home environment. It evaluates options such as heat pumps, gas furnaces, electric resistance heating, and other HVAC configurations by converting heating and cooling demand into realistic energy usage. These calculations are then tied directly to local utility pricing data, allowing the system to estimate monthly and yearly operating costs with much higher accuracy than standard calculators.

A key feature of Wattonomy is its incentives engine, which incorporates federal, state, and utility-level programs into the financial analysis. It identifies applicable tax credits and rebates based on system type and location, then applies eligibility rules and stackability logic to calculate true net costs. This includes both upfront savings from rebates and delayed benefits from tax credits, giving users a realistic picture of affordability and return on investment.

Wattonomy also emphasizes transparency through its AI explanation layer. Every recommendation is accompanied by clear, human-readable explanations that break down how costs were calculated, how incentives apply, and whether they provide meaningful short-term or long-term benefits. Importantly, all incentive-related claims are backed by verifiable sources, allowing users to review official documentation such as government programs and legal references.

Overall, Wattonomy functions as a modular and extensible system where energy modeling, financial analysis, incentives, and AI-driven explanations work together. Its plugin-based architecture allows new regions, utility providers, and energy systems to be added over time, making it a scalable foundation for transparent, data-driven energy decision-making.