MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202621051026 A) filed by Moresh Madhukar Mukhedkar; Marmik Patil; Gayatri Shelke; Dr. Shweta Koparde; and Dr. Vivek Patil on April 22, 2026, for Energiq: A Unified Framework For Global Energy Consumption Analysis.
Inventors include Moresh Madhukar Mukhedkar; Marmik Patil; Gayatri Shelke; Dr. Shweta Koparde; and Dr. Vivek Patil.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: A computer-implemented unified framework for global energy consumption analysis and demand forecasting, referred to as EnergiQ, is disclosed. The system integrates structured data preprocessing, SQL-based analytical processing, regression-based machine learning prediction, and interactive business intelligence visualisation within a single, cohesive platform. Raw global energy consumption datasets spanning multiple decades and countries are systematically cleaned, normalised, and enriched through feature engineering in Python. The processed data is stored in a relational SQL database that enables efficient macro-level querying, aggregation, and comparative analysis of energy usage patterns at country-wise and year-wise levels. The invention addresses critical limitations of conventional energy analysis tools, which are typically restricted to static historical reports and lack predictive intelligence. EnergiQ employs transparent regression models trained on key indicators such as population growth, GDP, and historical consumption trends to generate accurate forecasts of future energy demand for the next 5–10 years. These forecasts are accompanied by detailed comparisons between renewable and non-renewable energy sources, providing clear visibility into sustainability progress across different regions. The system produces structured outputs that include historical trends, growth rates, and predictive projections, all of which are designed to be easily interpretable by both technical analysts and non-technical policymakers. A key innovation of EnergiQ lies in its seamless integration of data analytics, machine learning, and visualisation. After forecasting, the system automatically exports both historical analysis and future predictions into Microsoft Power BI, where interactive dashboards, geographical heat maps, bar charts, pie charts, and drill-down filters are generated. Users can dynamically explore data by country, year, or energy type, instantly visualising complex trends that would otherwise remain hidden in traditional spreadsheets. This interactive capability significantly reduces the time required to derive actionable insights and makes the system accessible to a wide range of stakeholders, including government energy departments, researchers, and sustainability consultants. The architecture is deliberately designed to be lightweight and scalable. The entire pipeline operates efficiently on standard hardware such as laptops with an Intel i5 processor and 8 GB RAM, eliminating the need for expensive servers or cloud infrastructure. This low-resource requirement ensures that even smaller organisations and academic institutions can deploy and benefit from the system. Furthermore, the modular design allows individual components to be updated independently, making future enhancements straightforward without disrupting the overall workflow. By combining transparent regression-based forecasting with interactive visualisation, EnergiQ bridges the long-standing gap between complex data science and practical policy-making. The system not only analyses what has happened in the past.
Disclaimer: Curated by HT Syndication.
