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3 E Network Announces “AI Smart Energy Plan” for Mikkeli Project, Exploring Algorithm-Driven Approaches to Energy Economics
HONG KONG, Feb. 05, 2026 (GLOBE NEWSWIRE) -- 3 E Network Technology Group Limited (Nasdaq: MASK, "3 E" or the "Company"), a business-to-business ("B2B")
About this update from 3 E Network Technology Group Ltd
[{"type":"text","content":"HONG KONG, Feb. 05, 2026 (GLOBE NEWSWIRE) -- 3 E Network Technology Group Limited (Nasdaq: MASK, \"3 E\" or the \"Company\"), a business-to-business (\"B2B\") information technology (\"IT\") business solutions provider advancing toward next-generation artificial intelligence (\"AI\") infrastructure solutions, today announced the implementation of its \"AI Smart Energy Plan\" in connection with the AI Data Center in Mikkeli, Finland. Intended to serve as a top-level planning framework for the construction phase, this blueprint leverages the Company's proprietary innovations in the Internet of Things (IoT) and data intelligence. Through the design of five core technical modules, the plan is designed to support full-chain control over operational energy usage. The initiative aims to optimize Power Usage Effectiveness (PUE) via technological intervention, supporting a transition from traditional \"passive consumption\" toward \"active management.\" Amidst surging computing demand and fluctuating conditions in European energy markets, energy efficiency has become an increasingly important factor in the economics of data center operations. Dr. Tingjun Yang, CEO of 3 E, said: \"For high-density AI computing centers, electricity is not merely a cost but a strategic asset. The Company is designing the Mikkeli project with the objective of improving energy efficiency through algorithm-driven management approaches. By aligning computing workloads with real-time market signals, this framework is intended to support more efficient energy utilization over time, subject to market and operational conditions.\" The newly released Smart Energy Plan encompasses the following proposed key technical pillars: Construction of Omni-Domain High-Frequency Sensing System: Utilizing a dense IoT sensor network, the system is designed to generate a high-frequency, full-state digital mapping of the physical environment. By aggregating multi-dimensional data—spanning IT load characteristics, chiller operating status, and outdoor meteorological conditions—the system is designed to generate high-precision decision inputs for AI algorithms, with the objective of reducing blind spots and enhancing operational visibility.Implementation of AI-Adaptive Closed-Loop Tuning: Building upon and extending beyond traditional static configurations, the system is designed to ...