Blurred photo of two people in reflective vests walking between solar panels

Discover the capabilities of Case School of Engineering’s Solar Durability and Lifetime Extension (SDLE) Research Center at Case Western Reserve University. 


The purpose of the Energy Common Research Analytics and Data Lifecycle Environment (Energy CRADLE) is to create for engineering, and in particular lifetime science, the tools and protocols necessary to transform Big Data into information, which informs scientific knowledge to guide further analysis. Energy CRADLE is tightly focused on serving the needs of handling and sharing data among the SunFarm network researchers. Raw data collected from the SunFarms will go through data pre-processing and semantic annotation and stored in an NO-SQL Hadoop system. 

With domain knowledge, Energy CRADLE can manage the organization and orchestration of the data, making the inquiry of the data more efficient. The Energy CRADLE data integration environment has two features. First, it can push all the raw data collected from SunFarms onto a Hadoop Distributed File System (HDFS) and further map to HBase which is a distributed database. Secondly, through Thrift and REST servers, the user can use a visual front end to interact with data stored in HBase.

SDLE SunFarm

The SDLE SunFarm provides extensive outdoor exposure capabilities, including  fourteen Opel SF-20 dual axis trackers for samples and modules, with a capacity of more than 15,000 samples at 1-5x concentration, along with racking for fixed-mount modules. The SDLE Center also has collaborating partner outdoor exposure  facilities in  Arizona, Colorado, and Florida.

SunFarm Outdoor Trackers

Fourteen Opel SF-20 Trackers

  • Each tracker 4m x 5m
  • GPS-controlled tracking follows sun path, even on cloudy days

Fixed Panel Mounting for Further Capacity

  • Over 130 Modules, 8,000 Samples On-Sun
  • Samples at 1x, 2x, 4x, 5x concentration

Daystar Multitracer for Time-Series Analysis

  • Measure IV curves on two trackers every 5 seconds, 24/7
  • Extrapolate power degradation rates using data mining analysis