Global eco-friendly energy systems provider EcoFlow recently unveiled its next-generation smart home energy management system, OASIS 3.0, in Munich, Germany.

OASIS evolved from the energy management capabilities of EcoFlow’s app released in 2020, and is now the central pillar of EcoFlow’s intelligent energy ecosystem.

This is significant because in recent years, with the rapid adoption of residential photovoltaics, energy storage, electric vehicles (EVs), and dynamic electricity tariffs, home energy systems are undergoing a significant transformation from device management to energy orchestration, EcoFlow said. At the same time, a new wave of AI technologies, powered by large language models and agentic AI, are shaping the future of next-generation home energy management systems (HEMS).

Against this backdrop, EcoFlow hosted a roundtable forum entitled “Towards a Truly Proactive and Self-Evolving Home Energy System,” during the launch event. Representatives from academia, industry, user communities, and corporate technology sectors discussed core challenges in home energy management, new opportunities presented by agentic AI technology, and the future development of smart home energy systems.

Panelists highlighted the practical challenges still facing current home energy management systems.

Dr. Anurag Mohapatra, group leader at CoSES, the Munich Institute of Integrated Materials, Energy and Process Engineering (MEP), at the Technical University of Munich (TUM), noted that device integration and system interoperability remain widespread industry challenges. Although there have been claims of support for open protocols, compatibility issues between different devices and installation complexity continue to compromise user experience. Mohapatra further emphasized that there is still a lack of industry benchmarks, making system comparison difficult and preventing clear technical baselines across vendors.

Thomas Haupt, initiator and project manager of the HEMS-Finder research project, offered a market-centric view, noting that the primary concerns in the German home energy system market revolve around device communication capabilities, installation service infrastructure, and cross-system compatibility.

Haupt added that it is often unclear what each system is actually designed to do, making it difficult to compare them and effectively transfer knowledge to installers, which was the impetus for the HEMS Finder project. For most users, a system’s stability, reliability, and ease of installation often outweigh its complex intelligent features, he said.

From a technical standpoint, EcoFlow’s AI Technology Lead Dr. Xiaoke Yang observed that many HEMS still suffer from “functional silos,” where prediction, optimization, and user interaction are fragmented. Further, there is a significant gap between system operation and user understanding. Making system complexity transparent and comprehensible to users remains a major challenge, Yang said.

The rapid advancement of AI has frequently generated discussions about the impact of LLMs and agentic AI technology on the home energy sector. Over the past decade, Yang observed, HEMS has evolved from rule-driven to AI-prediction-driven stages. With the rapid advancement of LLMs and agentic AI, home energy systems are poised to enter a new phase, he said.

In this new phase, systems will no longer merely execute predefined logic and algorithms. Instead, they will understand user intentions and dynamically reconfigure functional units. For example, users could simply state, “I’m travelling next week, please minimize electricity costs,” or “We’re hosting a party this weekend, priorities comfort.” The system would then automatically translate these into energy optimization goals, intelligently coordinate generation, storage, charging, and household loads, and execute the resulting plan after user confirmation.

This “agentic HEMS”, capable of self-evolving based on user needs, ensures that AI serves user decisions rather than making decisions for them, Ecoflow said.