Summary
Solargis sought to transform their expertise in solar energy data analysis into a more automated data infrastructure. By leveraging statistical methods and machine learning models, we automated quality control processes and enhanced their data infrastructure. This resulted in significant efficiency gains, reducing manual work for data operators from hours to minutes.
Challenge
Motivation to change
Solargic wanted to create an effective transformation of their know-how in analysis of solar energy data.
Solution
Change delivery
By tackling the problem from both statistical and machine learning perspective we proposed comprehensive solutions covering a large spectrum of aspects.
We have automatized the quality control of various data related to the solar energy sector. We have built statistical models and simple machine learning models and helped to extend the data infrastructure and processes of this project.
Tools & means
- Docker
- Python
- Pandas, NumPy, SciPy
- Matplotlib
- Scikit-learn
- Keras, TensorFlow
Outcomes
Change outcomes
The project resulted in manual work reduction of data operators from hours to minutes.
Used services
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7 areasfound to be candidates for automation and optimization