Electrica announces the completion of the AIAE (Artificial Intelligence for Active Energy Management) project, developed in partnership with Romanian startup Renergia and the Technical University of Cluj-Napoca. This marks the launch of the first intelligent energy management system for buildings in Romania and introduces a new model for energy consumption management focused on data, automation, and advanced analysis.<\/p>\n\n\n\n
The platform has processed over 48 million datasets, and its development involved over 1,000,000 lines of code, demonstrating the complexity and maturity level of the solution. At the same time, the initiative also included a research and professional training component, with 3 doctoral students and 4 researchers under 30 years old involved, thus contributing to the development of a new generation of specialists in the field of energy and artificial intelligence.<\/p>\n\n\n\n
\u201eDigitalization is not a parallel activity. It is critical infrastructure for the energy system. Through AIAE, we moved from concept to implementation and demonstrated that artificial intelligence can deliver concrete results in building operations. We are talking about the ability to understand consumption in detail and to intervene where optimization is needed. This translates into control, predictability, and operational performance. The next step is to expand the solution to the entire Electrica Group,\u201d said Alexandru Chiri\u021b\u0103, CEO of Electrica.<\/p>\n\n\n\n
From monitoring to energy management<\/strong><\/p>\n\n\n\n The AIAE platform functions as a virtual energy manager, capable of analyzing energy consumption in real time and transforming large volumes of data into operational decisions. The system identifies consumption patterns, detects deviations, and generates automated recommendations for optimizing energy performance.<\/p>\n\n\n\n The solution is built on a multi-agent AI architecture and integrates data from multiple sources: electricity consumption, weather information, technical building characteristics, and operational context. This approach allows for a comprehensive understanding of energy behavior and supports rapid and efficient interventions.<\/p>\n\n\n\n Among the platform's main functionalities are:<\/p>\n\n\n\n \u2022 detecting energy consumption anomalies;<\/p>\n\n\n\n \u2022 energy consumption and production forecast;<\/p>\n\n\n\n \u2022 identification of energy efficiency measures;<\/p>\n\n\n\n \u2022 generating alerts for critical technical parameters;<\/p>\n\n\n\n \u2022 Optimization of self-consumption and reduction of carbon emissions.<\/p>\n\n\n\n An important element of the solution is the ability to \u201edecompose\u201d total energy consumption and understand how each building system contributes, without needing to monitor each piece of equipment individually. The platform uses artificial intelligence models to identify consumption patterns and show where and how energy efficiency can be improved.<\/p>\n\n\n\n Following the project, concrete energy efficiency measures have already been adopted, such as automating operating modes for certain systems and introducing policies for shutting down non-essential equipment outside working hours. These measures demonstrate the system's ability to generate applicable results, with a direct impact on reducing consumption and increasing operational efficiency.<\/p>\n\n\n\n \u201eFrom the outset, we aimed for this project to go beyond the research phase and reach operational status. We succeeded in integrating advanced models and numerical methods into a platform that operates in real-time within a complex environment. Artificial intelligence, in this context, provides precise tools for understanding energy behavior and optimizing consumption, based on data-driven decisions. It's an example of collaboration where research produces applicable results with a direct impact,\u201d stated Assoc. Prof. Dr. Eng. Dorin Beu from the Technical University of Cluj-Napoca.<\/p>\n\n\n\n \u201eFor Renergia, the AIAE project meant validating technologies developed in the research and innovation area on a real-world scale. We integrated disaggregation algorithms and predictive models into a system capable of operating autonomously and delivering concrete recommendations. The results obtained confirm that data-driven solutions can change the way buildings are managed, through better consumption control and more efficient use of energy resources,\u201d added the coordinators of the Cluj-based startup, Dr. Eng. Alexandru Mure\u0219an and Dr. Eng. Dacian Jurj.<\/p>\n\n\n\n Implement and expansion plans<\/p>\n\n\n\n The initial system integration was carried out at the Electrica company's headquarters, where it was tested and validated under real operating conditions. The results obtained confirm the platform's ability to generate optimizations and support more efficient energy consumption control. Based on these results, the company plans to replicate the solution across the Group's buildings as part of its digitalization and operational efficiency strategy.<\/p>\n\n\n\n The AIAE project has been extended to the \u201eProf. eng. Dimitrie Leonida\u201d National Technical Museum, making it the second building in Romania to benefit from this technology. The integration of the solution is part of the museum's modernization process and contributes to optimizing its energy consumption.<\/p>\n\n\n\n Renergia is a technology startup affiliated with the Technical University of Cluj-Napoca, specializing in the development of solutions for energy analysis and consumption optimization, using statistical models, probabilistic methods, and artificial intelligence.<\/p>","protected":false},"excerpt":{"rendered":" Electrica anun\u021b\u0103 finalizarea proiectului AIAE (Artificial Intelligence for Active Energy Management), dezvoltat \u00een parteneriat cu startup-ul rom\u00e2nesc Renergia \u0219i Universitatea Tehnic\u0103 din Cluj-Napoca. Acesta marcheaz\u0103 lansarea primului sistem inteligent de management al energiei pentru cl\u0103diri din Rom\u00e2nia \u0219i introduce un nou model de gestionare a consumului energetic axat pe date, automatizare \u0219i analiz\u0103 avansat\u0103. 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