Easiio | Your AI-Powered Technology Growth Partner

Client Background

XF is a global leader in vertical axis micro wind power generation, wholly owned by US capital, with technology originating from Germany and iteratively upgraded in Silicon Valley, USA. The company's mission is "Zero-carbon energy benefits the world" and focuses on: vertical axis wind turbine complete machine business, covering R&D, manufacturing, installation, and operation and maintenance full chain; energy management systems, integrating intelligent technology to optimize wind energy utilization efficiency.

XF New Energy - Vertical Axis Wind Turbine Production Base

Challenges Faced

Insufficient Sales Prediction Accuracy

Traditional statistical methods (such as time series analysis) are interfered by dynamic factors such as meteorology, policies, and regional economy, with error rates as high as 25%-30%, leading to increased inventory and supply chain costs

Multi-dimensional Data Integration Difficulties

Need to integrate multi-dimensional heterogeneous data such as wind turbine model performance data, regional climate characteristics, energy policy changes, and competitor dynamics

Real-time Requirements

Need to support quarterly rolling predictions, respond to rapid demand changes in emerging markets (such as Southeast Asia)

Core Objective

Build high-precision prediction models with error rates less than 10%, supporting global sales strategy optimization.

AI Technology Solution

Data Layer

Multi-source Data Fusion:

Internal data: Historical sales records (by model/region), wind turbine operation feedback, supply chain logs
External data: NOAA meteorological data, national carbon tax policies, macroeconomic indicators (GDP/electricity prices), competitor installation capacity (obtained through web scraping)

Feature Engineering:

Build derived features such as "wind resource utilization coefficient" (combining wind speed/turbulence intensity) and "sensitivity index".

Model Layer

Adopt multiple advanced machine learning models and model combinations:

Multi-dimensional data fusion, understanding market rhythm
Automatic learning optimization, getting smarter with use
Localized deployment, more responsive
Continuous validation, steady upgrades

Optimization Strategy:

Bayesian hyperparameter tuning, iteration efficiency improved by 40%
Introduce SHAP value analysis, quantify contribution of each feature (such as "Southeast Asia monsoon period" contributing ±8% to prediction bias)

Deployment and Iteration

Edge Computing:

Deploy lightweight models in China manufacturing center, supporting real-time prediction for regional service points

A/B Testing Mechanism:

New and old models run in parallel for 3 months, gradually validating stability

Project Results

8.5%

Prediction Error Rate

69.6% reduction from baseline

14 Categories

Data Coverage Dimensions

133% increase from baseline

30 Minutes

Model Response Speed

87.5% improvement from baseline

AI Technology Empowerment

Cross-domain technology integration, driving industry transformation

Pioneering "Wind Energy-Economy" Coupling Analysis Framework

Cross-domain technology integration: Combining meteorological science (WRF model output) with business prediction, pioneering "wind energy-economy" coupling analysis framework

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