Hybrid Artificial Intelligence model for photovoltaic plant project planning
We developed a prediction software with artificial intelligence for the prediction and management of tasks for the operation of a solar plant.
We developed a technology that integrates machine learning algorithms with expert systems, allowing a deep and detailed analysis of multiple variables and scenarios.
We developed a technology that integrates machine learning algorithms with expert systems, allowing a deep and detailed analysis of multiple variables and scenarios.
At Projener, we are proud to share a success story that stands out for its innovation and efficiency: the implementation of an artificial intelligence model based on artificial neural networks for the planning of photovoltaic plant projects. This project has not only optimised task completion time, but has also significantly improved resource management and operational efficiency.
Automated management
The planning and execution of PV power plant projects involves numerous tasks and phases that must be carefully coordinated to ensure the success of the project. Traditionally, this requires a great deal of time and human resources to develop accurate and realistic schedules.
To this end, Projener has developed pioneering software based on artificial neural networks, specifically designed to predict the completion time of each of the tasks required in the commissioning of a solar plant.
At PROJENER, we are committed to innovation and efficiency in the planning and execution of photovoltaic plant projects. Our latest success story highlights the use of a hybrid AI model that integrates machine learning algorithms and expert systems, providing a deep and detailed analysis of multiple variables and scenarios.
Components
of the sistem
Artificial neural networks
Time Prediction: Algorithms designed to predict the completion time of each task in the project.
Resource Optimisation: Improvement in the allocation and use of human and material resources.
Expert systems
Scenario Analysis: Evaluation of multiple scenarios and variables to generate accurate and realistic timelines.
Decision Making: Decision-making assistance based on historical data and current conditions.
User Interface
Automated Management: Tools for real-time project management and monitoring.
Alerts and Notifications: Alerts system to notify users about possible delays or problems.
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Workflow
Data Collection
Gathering historical and current data on photovoltaic plant projects.
AI Training
Training artificial neural networks with the collected data to improve prediction accuracy.
Scenario Analysis
Using expert systems to evaluate multiple scenarios and generate optimized schedules.
Prediction and Planning
Predicting task completion times and planning resources.
Monitoring and Management
Real-time monitoring of project execution, with the ability to adjust and re-plan as needed.
Benefits
Efficiency in
Project Management
Time optimization
Better management of
resource management
The hybrid AI model developed by Projener.ai represents a significant breakthrough in PV plant planning. The combination of machine learning algorithms and expert systems not only optimises time and resources, but also improves operational efficiency and ensures the success of projects.