Fraunhofer Chile Research
Fraunhofer Chile Research

Data Science & Analytics

Data Science & Analytics

In an era increasingly dominated by the incorporation of renewable energy sources, the relevance of photovoltaic technology is becoming more and more evident. With the growing need to exploit solar energy, large volumes of sensor-generated data are emerging, which, if properly processed, have the capacity to provide a vast array of invaluable information.

 

Our service proposition is geared towards assisting companies in the solar energy sector to maximize the utilization of the data produced daily in the industry. Through rigorous and comprehensive analysis of this data, we are in a position to provide critical intelligence that enables companies to make informed strategic decisions. Additionally, we facilitate the identification of asset failures and the detection of performance losses, thus contributing to the increase of energy efficiency in their respective systems.

Main applications

Solar resource assessment

Before installing solar panels, it is crucial to evaluate the amount of solar radiation reaching the location where the system is to be installed. Historical solar radiation data can be very useful for estimating energy production, as well as for determining the optimal location and orientation of the solar panels.

System design and simulation

Once the amount of available solar radiation is known, it is possible to design and simulate the system to optimize its production. For this purpose, simulation and modeling tools are used to determine the energy generation capacity, size the system appropriately and evaluate its profitability.

Monitoring & Control

In order to ensure optimal energy production, constant monitoring and control of the solar system is crucial. Performance and consumption data are collected and analyzed in order to detect problems, perform preventive maintenance and improve overall system performance.

Data analysis for decision-making

Data collected over time is useful for predictive analysis and strategic decision making around the solar system. An example of this is the use of historical data analysis to forecast long-term energy production, predict system failures, and plan preventive maintenance efficiently.

Continuous Improvement

Data analytics has valuable application in the continuous improvement of the solar system. Machine learning techniques are employed to detect patterns and trends in the data to improve energy efficiency, lower costs and increase profitability.

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