3 E Network Announces “AI Smart Energy Plan” for Mikkeli Project, Exploring Algorithm-Driven Approaches to Energy Economics

Rhea-AI Summary

3 E Network (NASDAQ: MASK) announced an AI Smart Energy Plan for its Mikkeli, Finland AI data center on Feb 5, 2026. The framework uses five technical modules—dense IoT sensing, AI closed-loop cooling, price-forecast models, economic workload dispatch, and demand-response integration—to optimize PUE and align compute with market signals.

The plan aims to shift from "passive consumption" to "active management," enabling workload scheduling to off-peak windows and potential participation in grid balancing while adhering to Nordic environmental standards.

News Market Reaction – MASK

+0.44%

2 alerts

+0.44% News Effect

-12.8% Trough Tracked

+$23K Valuation Impact

$5M Market Cap

0.3x Rel. Volume

On the day this news was published, MASK gained 0.44%, reflecting a mild positive market reaction. Argus tracked a trough of -12.8% from its starting point during tracking. Our momentum scanner triggered 2 alerts that day, indicating moderate trading interest and price volatility. This price movement added approximately $23K to the company's valuation, bringing the market cap to $5M at that time.

Data tracked by StockTitan Argus on the day of publication.

Key Figures

Project capacity: 26MW Convertible facility size: $2,000,000 2025 revenue: US$4,835,167 +5 more

8 metrics

Project capacity 26MW AI data center capacity target in Finland under Master Services Agreement

Convertible facility size $2,000,000 Maximum original issue discount convertible advances under securities purchase agreement

2025 revenue US$4,835,167 Fiscal year ended June 30, 2025 per Form 20-F

2024 revenue US$859,344 Prior fiscal year revenue per Form 20-F

2023 revenue US$37,130 Earlier fiscal year revenue per Form 20-F

Land lease term 30 years Land lease agreement term for Mikkeli data center site

Site area 101,071 square meters Designated Mikkeli data center parcel size

Registered resale shares 44,694,292 shares Class A Ordinary Shares registered for resale on Form F-1

Market Reality Check

Price: $2.00 Vol: Volume 471,201 versus 20-...

low vol

$2.00 Last Close

Volume Volume 471,201 versus 20-day average 4,421,830 indicates relatively muted trading activity into this news. low

Technical Price 0.229 is trading below the 200-day moving average at 1.33, reflecting prior downside pressure.

Peers on Argus

MASK is up 1.33% while key technology/software peers like FTFT, IFBD, BNZI, IDAI...

MASK is up 1.33% while key technology/software peers like FTFT, IFBD, BNZI, IDAI, and SGN show negative moves between -4.38% and -11.83%, pointing to a stock-specific reaction rather than a sector-wide AI or software rally.

Previous AI Reports

5 past events · Latest: Feb 03 (Positive)

Same Type Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Feb 03	Procurement launch	Positive	-2.2%	Began sourcing long-lead items for Mikkeli AI data center build-out.
Jan 29	Platform launch	Positive	+13.7%	Introduced Intellisight platform for AI cluster operations, security, and monitoring.
Jan 20	Deployment plan	Positive	-10.6%	Outlined high-density AI infrastructure deployment strategy in Finland with liquid cooling.
Jan 14	Architecture reveal	Positive	-18.0%	Detailed next-gen AI data center architecture targeting higher density and lower PUE.
Dec 15	Services agreement	Positive	+36.4%	Signed Master Services Agreement with Orka to develop 26MW Finland facility.

Pattern Detected

AI-related announcements tied to the Finland data center and AI platforms have produced mixed reactions, with some strategic updates selling off and others attracting sharp buying interest.

Recent Company History

Over recent months, MASK has focused on building an AI data center footprint in Finland and launching supporting software platforms. Key steps include a Master Services Agreement targeting at least 26MW, unveiling next‑gen data center architecture, and accelerating high‑density AI deployment. Subsequent updates covered strategic procurement and the Intellisight™ operations platform. Today’s AI Smart Energy Plan fits this sequence, adding an energy‑management layer to the existing Mikkeli project framework.

Historical Comparison

+3.9% avg move · Across recent AI-tagged announcements, MASK’s average move was about 3.85%, with both sharp gains an...

AI

+3.9%

Average Historical Move AI

Across recent AI-tagged announcements, MASK’s average move was about 3.85%, with both sharp gains and notable pullbacks following Finland data center updates.

AI-tagged news traces a build-out path: from a master services agreement and capacity plan, to unveiling core data center architecture, then detailed Finland deployment, platform launches, procurement steps, and now an AI-driven energy management framework for the Mikkeli site.

Market Pulse Summary

This announcement adds an AI-driven energy management layer to MASK’s Mikkeli AI data center project...

Analysis

This announcement adds an AI-driven energy management layer to MASK’s Mikkeli AI data center project, targeting improved PUE and workload scheduling based on real-time price signals. It follows prior AI-tagged steps: a 26MW capacity plan, detailed architecture, deployment strategy, and procurement. Investors may watch how algorithmic control, Demand Response integration, and OPEX optimization translate into margins alongside existing financing and the company’s US$4,835,167 in 2025 revenue.

Key Terms

power usage effectiveness (pue), internet of things (iot), machine learning, time-series forecasting, +4 more

8 terms

power usage effectiveness (pue) technical

"The initiative aims to optimize Power Usage Effectiveness (PUE) via technological intervention"

Power Usage Effectiveness (PUE) is a simple ratio that compares the total energy consumed by a data center (including cooling, lighting and other facility systems) to the energy used solely by the servers and networking equipment. For investors, a lower PUE means less energy is wasted — like a car that gets more miles per gallon — which usually translates into lower operating costs, smaller sustainability risks and a clearer picture of infrastructure efficiency.

internet of things (iot) technical

"this blueprint leverages the Company's proprietary innovations in the Internet of Things (IoT)"

The internet of things (IoT) describes a network of everyday objects—such as appliances, vehicles, and devices—that are connected to the internet and can share data automatically. This connectivity enables these objects to function more efficiently and provides valuable insights for businesses and consumers alike. For investors, IoT represents a growing area of technological innovation with the potential to transform industries and create new market opportunities.

machine learning technical

"the system is designed to employ machine learning models to execute closed-loop control"

Machine learning is a set of computer programs that learn patterns from large amounts of data and improve their predictions or decisions over time, like a recipe that gets better each time it’s adjusted based on taste tests. For investors it matters because these systems can speed up analysis, spot trends or risks humans might miss, automate routine work, and potentially create competitive advantages or cost savings that affect a company’s performance.

time-series forecasting technical

"this module is designed to utilize time-series forecasting technology to decode grid"

Time-series forecasting is the practice of using past data points that are ordered by time—such as daily prices, monthly sales, or quarterly earnings—to predict what will likely happen next. It matters to investors because these forward-looking estimates help with decisions like valuing an asset, timing trades, planning cash needs, and managing risk; think of it as using past weather patterns to estimate tomorrow’s conditions so you can plan accordingly.

service level agreement (sla) technical

"while seeking to maintain the Service Level Agreement (SLA) of critical tasks"

A service level agreement (SLA) is a written promise between a service provider and its customers that spells out measurable expectations—such as how often a service will be available, how fast problems will be addressed, and what compensation applies if those targets aren’t met. Investors care because SLAs reveal how reliable a business must be to keep customers and revenue, and they highlight potential costs or penalties, much like a warranty or on-time delivery promise that affects a company’s reputation and predictable cash flow.

operational expenditure (opex) financial

"This strategy is intended to support optimization of the Operational Expenditure (OPEX) structure"

Operational expenditure (opex) are the regular, day-to-day costs a company must pay to keep the business running—wages, rent, utilities, supplies, and routine maintenance. Investors care about opex because it directly reduces profit and cash flow: well-controlled opex can boost margins and returns, while rising or unpredictable opex can eat into earnings even if sales grow, much like household bills cutting into what you can save each month.

demand response technical

"Through integrated Demand Response modules, the facility is designed to support"

Demand response is a program or market mechanism where electricity users are paid or incentivized to reduce or shift their power use when the grid is stressed or prices are high, similar to turning down nonessential appliances during a heat wave to ease a traffic jam. It matters to investors because it can lower peak energy costs, affect utility revenues and market prices, and create opportunities for companies that provide the software, equipment, or services that enable those load changes.

ai data center technical

"in connection with the AI Data Center in Mikkeli, Finland"

An AI data center is a specialized facility that houses powerful computers, networking gear, and cooling systems designed specifically to run and store artificial intelligence workloads, like training large models and serving real-time AI applications. Investors care because these centers are capital-intensive infrastructure that enable companies to offer advanced AI services, drive recurring revenue, and create competitive advantages, much like a factory that determines how quickly and cheaply a business can produce its product.

AI-generated analysis. Not financial advice.

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 employ machine learning models to execute closed-loop control over cooling strategies. It is intended to dynamically align fan speeds and refrigerant flow rates with real-time thermal loads, supporting precise "cooling-on-demand." This mechanism is expected to help reduce energy waste, ensuring the facility continuously operates within its optimal designed PUE range.
• Development of High-Precision Price Prediction Models: Tailored to the characteristics of the regional power market, this module is designed to utilize time-series forecasting technology to decode grid supply and demand trends. It is intended to generate forward-looking price trend signals that may serve as an important input for economic workload scheduling.
• Establishment of Economic Workload Dispatch Mechanism: Creating a synergistic "Compute-Energy" response protocol. Guided by predictive pricing signals, the system is designed to support automate task orchestration: while seeking to maintain the Service Level Agreement (SLA) of critical tasks, it automatically migrates high-intensity large-scale training workloads to off-peak price windows. This strategy is intended to support optimization of the Operational Expenditure (OPEX) structure.
• "Passive Consumption" to "Active Management": Reframing the interaction logic between the infrastructure and the power grid. Through integrated Demand Response modules, the facility is designed to support bidirectional regulatory capabilities. The system may adjust power consumption to assist grid balancing based on utility instructions to support grid balancing efforts, with the potential to integrate more closely into the local green energy ecosystem and explore ancillary service opportunities.
  
  

This "AI Smart Energy Plan" represents a strategic framework developed by the Company that draws on its technological capabilities to support the operational performance of the Mikkeli project. In an era where energy efficiency plays an increasingly important role in AI computing margins, this algorithm-driven management model is intended to enhance the Company's resilience against energy price fluctuations over time, supporting more sustainable cost management. Furthermore, by embedding "green sustainability" into its core business logic, the plan is designed to align with strict Nordic environmental standards and may serve as a standardized reference framework for future global expansion, supporting a competitive position within the industry.

About 3 E Network Technology Group Limited

3 E Network Technology Group Limited is a business-to-business ("B2B") information technology ("IT") business solutions provider, committed to becoming a next-generation artificial intelligence ("AI") infrastructure solutions provider. It upholds the industry consensus of "AI and energy symbiosis" and has excellent vision in the field of energy investment. The Company's business comprises two main portfolios: the data center operation services portfolio and the software development portfolio. For more information, please visit the Company's website at https://3emask.com/.

Forward-Looking Statements

Certain statements in this announcement are forward-looking statements. These forward-looking statements involve known and unknown risks and uncertainties and are based on the Company's current expectations and projections about future events that the Company believes may affect its financial condition, results of operations, business strategy and financial needs. Investors can identify these forward-looking statements by words or phrases such as "approximates," "assesses," "believes," "hopes," "expects," "anticipates," "estimates," "projects," "intends," "plans," "will," "would," "should," "could," "may" or similar expressions. The Company undertakes no obligation to update or revise publicly any forward-looking statements to reflect subsequent occurring events or circumstances, or changes in its expectations, except as may be required by law. Although the Company believes that the expectations expressed in these forward-looking statements are reasonable, it cannot assure you that such expectations will turn out to be correct, and the Company cautions investors that actual results may differ materially from the anticipated results and encourages investors to review other factors that may affect its future results in the Company's registration statement and other filings with the U.S. Securities and Exchange Commission.

For more information, please contact:

3 E Network Technology Group Limited

Investor Relations Department

Email: ird@3emask.com

Website: https://3emask.com/

FAQ

What is the AI Smart Energy Plan announced by 3 E Network (MASK) for Mikkeli on Feb 5, 2026?

It is a five-module framework to optimize data center energy use via algorithmic controls and market signals. According to the company, it combines high-frequency IoT sensing, AI closed-loop cooling, price forecasting, economic workload dispatch, and demand-response integration.

How will MASK's economic workload dispatch affect AI training jobs at Mikkeli?

It intends to shift high-intensity training to lower-cost, off-peak windows while preserving critical SLAs. According to the company, predictive price signals guide automated task migration to optimize operational expenditure.

What energy-efficiency technologies does 3 E Network (MASK) plan to use in Mikkeli?

The plan uses dense IoT sensing, AI-adaptive closed-loop cooling, and price-prediction models for dynamic control. According to the company, these modules generate high-frequency state data and drive cooling and workload decisions to improve PUE.

Will the MASK Mikkeli project participate in grid demand-response or ancillary services?

The plan is designed to support bidirectional demand-response and assist grid balancing under utility instructions. According to the company, integration may enable closer ties to local green energy and explore ancillary service opportunities.