Missing data and poor data quality can have serious impact on your solar analytics and overall performance of your solar PV plant. Read to know how you can solve this issue using AI-based Data Automation technology.
As solar companies are shifting towards data-driven decisions, it becomes critical to have good quality data to ensure analysis is correct and actionable.
What is the role of data in solar PV plant’s performance?
Data not only helps you understand how your solar PV plant is performing in general but also helps to compare and analyze how different sections of the solar plant are performing in comparison to each other using performance indicators like PR, SY, soiling, degradation, etc. For example, data can tell you precisely if a panel is degrading faster than the other panel installed at the same site due to a shadow from the nearby tree. Data can derive intelligence for multiple stakeholders: Sales, engineering, procurement, management and investors. In short, data intelligence empowers a solar company to take informed decisions and plan their solar O&M in a better way.
The curious case of missing solar data points
Generally, a MW size of a solar PV plant produces 100,000 data points in a month. However, as reported by our customers in multiple geographies, 16-20% data points are usually missing in this data due to network or connection failure and/or faulty readings by the data loggers. Because of the huge quantity of data produced otherwise, missing data is usually ignored (an easy way out), thereby creating a void that leads to incorrect conclusions.
For example, missing data during the sunshine hours can effectively lead to low PR value in days of high energy production, or measurements during night time hours can give a high CUF value which is misleading.
Also, it is quite common to receive outliers/ bad quality measurements from data loggers, pyranometers due to soiling, bad weather conditions, calibration issues, etc. Generally, as a part of data cleaning, this data is removed from the dataset leading to data loss.
AI-based Data Automation tool for any kind of solar PV plant
SmartHelio’s Data Automation Tool aims to provide clean and sanitize data in a single click.
Our Data Automation tool is designed to clean and sanitize in a way to first remove bad measurements from the dataset and clean the dataset. It later predicts the missing data points with 99% accuracy using Machine Learning algorithms and provides sanitized data. All you need to do is connect your plant via API or upload your data to the tool and click “Submit”. You can simply download sanitized data and use it for your internal analysis.
To know more about our Data Automation tool you can book Ask Me Anything (AMA) session with our solar experts.
