Renewable Energy Scenario Generation with Controllable Features Using Configurable Deep Convolutional Generative Adversarial Network

Key Points

• The scenario generation process successfully models renewable energy configurations, enhancing predictive accuracy.
• Generative adversarial networks can create realistic energy scenarios from diverse inputs, with results showing a notable accuracy increase.
• Deep convolutional generative adversarial networks were employed to analyze energy configurations in multiple simulations.
• This approach may enable more efficient energy planning and management, though application scalability remains to be tested.