Solargis is a growing business providing an online platform with advanced data analytics helping solar power projects from pre-feasibility and planning to operations and management. The platform includes reliable weather forecasting leading to energy production simulation that enables seamless integration to the energy market.
"Ableneo enabled effective transformation of our know-how in analysis of solar energy data into a more automated data infrastructure."
Summary
We helped Solargis to extend their data infrastructure and processes by applying advanced statistical data models and machine learning. This led into manual work reduction of data operators and efficiency increase.
Challenge
Motivation to change
The data feeds to the Solargis’es application required significant manual work, which limited the company’s operational efficiency and was inherently prone to human errors.
Solution
How we addressed it
Ableneo’s mission in the project started with analysis of the areas where automation would make a difference. There were seven areas identified for automation and optimization.
The subsequent automation of the selected areas was approached with a combination of statistical and machine-learning methods, leading to a robust solution.
Our mission in the project included:
- Helping to extend the company’s data infrastructure and processes around it.
- Automation of the quality control of various data points related to the solar energy sector.
- Fault detection of solar photovoltaic power plants.
- Building statistical models and simple machine learning models.
Tools & means
- Docker
- Python
- Pandas, NumPy, SciPy
- Matplotlib
- Scikit-learn
- Keras
- Tensor flow
Outcomes
What has changed
Having implemented the advanced statistical data models and machine learning features, the manual work of data operators was reduced from hours to minutes.