SOLAR AÄ°
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Solar AI is developed by Marten Company; It is an artificial intelligence software platform that analyzes solar energy and energy systems, evaluates their performance and supports operational decision making.
Unlike classical monitoring solutions, the system does not only provide data.
It analyzes collected data, interprets system behavior and produces meaningful outputs about efficiency, risk and performance.
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Key Features
System Analysis
Solar AI develops the energy system not through individual values;
It analyzes by evaluating all parameters together.
The main data examined:
solar radiation
instant production
panel surface condition
angle and direction information
heat
environmental impacts
By evaluating these data together, the real state of the system is determined.
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Intelligent Software Layer for Solar Energy Systems
Solar AI is Marten's smart system approach developed for solar energy infrastructures.
To this end; Our aim is to make solar energy systems smarter in terms of efficiency and operation by providing only production equipment.
The Solar AI vision includes the following areas:
interpretation of production logic,
analyzing system efficiency,
assessing the impact of dirt and maintenance,
Optimizing sun tracking logic,
Emergence of protection against external risks such as hail, rain and storm,
The beginning of the digital engineering experience describing the automatic system.
Solar AI is now not just a panel software;
It is considered as a smart platform that can be scaled to large energy systems in the future.
Performance and Efficiency Evaluation
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Solar AI doesn't just measure production;
Compares expected production with actual production.
In this way:
The yield rate is calculated
performance decreases are detected
The reason for production losses is understandable
Anomaly and Fault Detection
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The system uses behavioral analysis rather than thresholds.
The following situations are automatically detected:
Low production despite high irradiance
unexpected fluctuations
sensor inconsistencies
performance anomalies
For each situation:
probable cause
risk level
recommended action
is produced.
Intelligent Cleaning Management
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Solar AI does not clean the panel according to fixed times;
plans based on performance impact.
The contamination level is analyzed
The impact on production is measured
Appropriate cleaning time is determined
This approach reduces unnecessary operations and maintains efficiency.
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Thermal Analysis
Solar AI monitors the relationship between temperature and performance.
overheating tendencies
temperature-induced efficiency loss
system stress state
detected and early warning provided
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Environmental Risk Management
The system also takes into account environmental factors:
rain
full
sudden weather changes
In these cases, system behavior is analyzed and a protection scenario is recommended if necessary.
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Explainable Artificial Intelligence
Solar AI presents technical outputs to the user in an understandable manner.
For example:
technical output: efficiency decrease
Explanation: “It may be due to contamination on the panel surface”
In this way, the system is not only for engineers but also
It also becomes available to operations teams.
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Working Structure
Solar AI works in three key stages:
1. Data Collection
Data is received from sensors and systems.
2. Analysis
The data is compared with expected values ​​and historical behavior.
3. Decision
System:
detects anomaly
produces risk
suggests action
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Learning and Development Process
Solar AI is developed with an extensive data and simulation infrastructure.
Simulation Based Training
The system is trained through different scenarios:
production declines
pollution effects
temperature changes
sensor errors
combination situations
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Multiple Data Analysis
The model is not based on a single data point;
It learns by looking at multiple parameters at the same time.
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Normal and Abnormal Behavior
The system both:
normal working conditions
and malfunction situations
learns.
In this way, it produces more accurate and balanced results.
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Continuous Improvement
Solar AI in the future:
collecting data from the field
learning new scenarios
by updating itself
will continue to develop.
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Sun Tracking Optimization
The system analyzes the position of the panel relative to the sun.
angle deviation is detected
The impact on production is evaluated
Correction suggestion is offered
The aim is to maintain the maximum production level.
Collective Learning AI
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Solar AI is not just software that runs on a single system.
The platform is built on a constantly evolving collective learning structure by combining experiences from different energy systems.
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How Does It Work?
Solar AI analyzes the situations occurring in each system and not only uses this information locally.
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1. Gathering Experience
In an energy system:
fault
efficiency drop
abnormal behavior
environmental impact
When such situations occur, Solar AI analyzes this process.
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2. Learning and Model Update
Data obtained:
is classified
is analyzed
associated with system behavior
As a result of this process, the model learns a new situation.
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3. Global Information Sharing
This information learned:
transferred to other Solar AI systems
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Information learned in a system
becomes available for all systems.
Sample Scenario
In a region:
Efficiency decreases due to high temperature
the system analyzes this and learns
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when a system at a different location enters similar conditions
👉 Solar AI recognizes this situation in advance and:
gives early warning
recommends the right action
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Advantages Provided
Faster learning system
Faster adaptation to new situations
Higher accuracy rate
Continuously evolving artificial intelligence model
Strategic Value
Thanks to this structure, Solar AI:
It ceases to be a singular software
becomes a network that learns from connected systems
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Solar AI is not just a working system;
It is an artificial intelligence platform that improves with every new experience and makes all systems smarter.