PROJECT HIGHLIGHTS

Long-term forecast for 6 months or more

High accuracy (over 95%)

Adjustability for any region of the world

Low resource using for neural network learning

SKILLS & TECHNOLOGIES

Using the most advanced technologies in programming neural networks and computer training based on past periods

Development
Design
Marketing
Testing
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Days of Work
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Specialists
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Success test
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Prototypes Done

SOME DETAILS

  • Neural Network Model for Carbon Emission Estimation Problem.
  • A multi-layer neuro chain structure is used to estimate the CO2¬†emission.
  • The neural network consists of a group of receptors, 4 hidden layers and one output neuron.
  • The group of receptors is supplied with a segment of historical CO2 emission data.
  • Each hidden layer of the neural network consists of n-neurons, each next layer is fully connected with the following one.
  • The function of activating hidden blocks ensures non-linearity of the network.
  • The network was trained by using the Back Propagation (BP) algorithm.
  • The output layer consists of one output neuron which is producing an appropriate
  • CO2 emissions.
  • The node of the output level has a sigmoidal activation function.