A web application for calculating solar potential and system configuration with an interactive map for panel placement, energy-consumption modeling, and report generation for presentations and integrations.Â
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A solar calculator for planning and deploying solar energy systems. Users select a location, see generation potential against real weather data, and visually configure panel placement on the map. The app analyzes electricity consumption and generates recommendations on required panel configurations and the potential savings. Results can be returned as JSON for integrations or generated as PDF reports for client presentations or internal use.
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Malogica Solar is a sustainability initiative founded by Jörg Weishaupt, a tech entrepreneur with 30+ years of experience building IT companies in Germany, Armenia, the United States, and the Philippines. The company builds tools that make solar energy more accessible and easier to evaluate for both individuals and businesses, with a strong emphasis on calculation accuracy and practical applicability. For a product like this, the data has to stay relevant to a specific location, and the interface has to let users make decisions quickly — even without a technical background. It was equally important to lay the foundation for further platform growth and the addition of new data sources and scenarios, including e-commerce integrations.
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Data accuracy
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Weather and solar data had to be integrated to ensure calculations are tied to a precise point on the map and reflected real conditions.
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Multiple data sources
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Data from different systems — weather JSON feeds, geospatial data, model parameters — had to be unified into a single structure without breaking consistency, and with room to grow.
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Interactive map
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The map had to function as a planning tool — letting the user place panels and immediately see the impact on the calculations.
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Energy consumption
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The platform needed a model that could analyze consumption and produce practical recommendations on the system and potential savings.
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Stable architecture
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The application had to be ready for new data sources and future extensions, including e-commerce scenarios.
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A clear interface
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Despite the complexity of the underlying logic and data, the user experience had to stay simple and provide a fast path from picking a location to a result the user could understand and act on.
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We built the application with a clean separation between a managed backend and a high-performance frontend. The Next.js/React frontend with Tailwind carry fast interaction, responsiveness, and visual clarity across the scenarios. The backend is built around Directus together with Node.js/Express.js for API operations and data management, so the platform stays scalable and ready for further development. We also integrated weather and solar data sources, plus a reporting layer: JSON for integrations and PDF for presentations.
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Next.js frontend with Tailwind
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We structured the interface as a sequential flow — starting with location selection, then potential review, map interaction, parameter setup, and finally the result. Tailwind is used for responsive layouts and a clean, functional UI.
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Backend core on Directus + Node.js/Express.js for API workflows
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We used Directus as the system for managing data and configurations, with Node.js/Express.js as the foundation for stable API interactions and data-flow processing. This keeps data structured and manageable and prepares the platform for future extensions.
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Integration with Google Solar APIs and weather JSON feeds
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We integrated real-time data sources for calculating solar potential at a specific geolocation. To do that, we used Google Solar APIs together with custom processing scripts — including solar-exposure and forecasting logic — so the results were practically useful for planning.
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Interactive map on Google Maps API for panel placement
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We implemented the map as a working tool where the user can visualize a location and work through panel placement, with a direct link between map actions and calculation results.
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Energy consumption models in TypeScript for personalized recommendations
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We built consumption-analysis models that produce recommendations on system configuration and estimates of potential savings.
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Reporting and result output: JSON and PDF
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We implemented JSON outputs for integration with external systems and PDF generation for presentations and commercial proposals. We also designed modular templates so the visualization of potential savings and system parameters could evolve without core changes.
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At the start we defined the core scenarios: potential calculation by geolocation, map interaction, consumption analysis, recommendation generation, and reporting. We aligned on what data feeds in, how it should shape the result, and what level of accuracy and stability we expected. Separately, we defined which data sources could be added later and how the platform would absorb them. That defined the architecture and the implementation order.
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+2 Resource
The client now has a solar-planning tool that combines real data, map-based work, and practical recommendations grounded in consumption modeling. The platform is ready for new data sources, and feature growth doesn't require rebuilding the core.
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90% of calculations return within 2–4 seconds
caching and map-level debounce keep Google Solar and weather-API spend under control
identical inputs produce identical results
to generate a presentation-ready PDF report with locations, key figures, and assumptions
with no need to rewrite the core
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