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3 Latest Announced Rounds

  • $3,500,000
    Seed

    1 Investors

    Technology, Information and Internet
    Dec 20th, 2024
  • $5,619,170
    Series B

    1 Investors

    Research Services
    Dec 20th, 2024
  • $8,000,000
    Unknown

    5 Investors

    Computer & Network Security
    Dec 20th, 2024
$671.88M Raised in 44 Funding Rounds in the past 7 Days - View All

Funding Round Profile

ANNEA

start up
Germany - Hamburg, Hamburg
  • 15/12/2023
  • Seed
  • $3,012,000

ANNEA provides the next generation of condition-based predictive maintenance and underperformance detection for renewable energy assets. It enables automated drive train vibration analysis, based on cutting edge artificial intelligence, physical modelling, and normal behaviour modelling.

Operators receive notifications about future failures up to 365 days in advance, containing detailed information on which component has a problem, the root cause of the problem, and the best time window for repair. Besides, ANNEA detects underperformance of renewable assets and helps to maximise energy production.

We optimise operations and maintenance across renewable energy sectors, reducing unplanned downtime and lost profit significantly, making green energy more affordable. Let's accelerate the energy transition together!


Related People

Maik RederFounder

Maik Reder Germany - Greater Hamburg Area

• I hold an international PhD degree in artificial intelligence algorithms and advanced reliability models for condition monitoring and failure detection in wind turbines.

• Certified Advanced Data Science Specialist by IBM

• International academic and industrial research experience within the EU´s Marie-Curie Innovative Training Network (CIRCE, DTU, ENEL Green Power) in artificial intelligence, cloud computing, reliability modelling, physical and statistical modelling, data mining, numerical and computational fluid dynamics, hydrodynamics as well as aero-elastic analysis.

• Academic Background: Master of Science in Mechanical Engineering specialised in Aeronautical Engineering and Sustainable Energy Systems (Technical University of Munich).

• Proficiency in several programming languages such as: R, Python and Matlab as well as their extensions and packages for data science and machine learning. Experienced in aero-elastic codes (FAST, HAWC2) and hydrodynamics (WAMIT) for wind turbines and floating offshore platforms.

• Languages: Native German speaker; fluent in English, Spanish and Portuguese (CEFR: C1-
C2); and basic knowledge in Italian, French and Danish.