A5062226684- Photovoltaic System Optimization Techniques
- Solar Radiation and Photovoltaics
- Building Energy and Comfort Optimization
- Solar Thermal and Photovoltaic Systems
- solar cell performance optimization
- Energy Efficiency and Management
- Smart Grid Energy Management
- Industrial Vision Systems and Defect Detection
- Quality and Safety in Healthcare
- Healthcare Technology and Patient Monitoring
- Cloud Computing and Resource Management
- Wind and Air Flow Studies
- Photovoltaic Systems and Sustainability
- Electrical Fault Detection and Protection
- Image Processing Techniques and Applications
- Energy Load and Power Forecasting
- Sustainable Building Design and Assessment
- Machine Learning in Materials Science
- Time Series Analysis and Forecasting
- CCD and CMOS Imaging Sensors
- Green IT and Sustainability
- Chemical and Physical Properties of Materials
Case Western Reserve University
2013-2020
An automated data analysis pipeline is developed to preprocess electroluminescence (EL) module images, and parse the images into individual cells be used as an input for machine learning algorithms. The dataset in study includes EL of three 60 cell modules from each five commercial brands at six steps damp heat exposure, 500 3000 h. Preprocessing original raw lens distortion correction, filtering, thresholding, convex hull, regression fitting, perspective transformation produce planar...
Based on recent advances in nanoscience, data science and the availability of massive real-world datastreams, mesoscopic evolution energy materials can now be more fully studied. The temporal is vastly complex time length scales fundamentally challenging to scientific understanding degradation mechanisms pathways responsible for over lifetime. We propose a paradigm shift towards modeling, based physical statistical models, that would integrate laboratory studies datastreams into...
A nonrelational, distributed computing, data warehouse, and analytics environment (Energy-CRADLE) was developed for the analysis of field laboratory from multiple heterogeneous photovoltaic (PV) test sites. This informatics infrastructure designed to process diverse formats PV performance climatic telemetry time-series collected a outdoor network, i.e., Solar Durability Lifetime Extension global SunFarm as well point-in-time spectral image measurements material samples. Using Hadoop/HBase...
A data set of 90 60-cell module images from 5 commercial PV brands over 6 exposure steps damp-heat testing were analyzed. An automated analysis pipeline was developed using the open source coding language Python to parse into individual cell images. As original raw are not directly suitable for modeling, this algorithm implements techniques which include filtering, thresholding, convex Hull, regression fitting, and perspective transformation pre-process image. After extraction, 5400 as a...
Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study the thermal performance 24 new (Enphase M215) connected to 8 different brands PV modules on dual-axis trackers at Solar Durability Lifetime Extension (SDLE) SunFarm Case Western Reserve University, based minute by power data from along with insolation environmental July through October 2013. The analysis shows strengths...
Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual audits of readily available 15-minute-interval electricity consumption are being explored provide quick, inexpensive, useful insights into operation characteristics. A cross sectional analysis six buildings in two different climate zones provides methods for data cleaning, population-based comparisons, relationships (correlations)...
Rigorous statistical analysis of whole building, 15-minute interval, time series electricity data enables remote insights into buildings' operational characteristics. We developed select building markers and applied them to six commercial office buildings located in three different climate zones for comparison. The reveal information about daily patterns, scheduling, the ratio base peak load. Time analysis, clustering, anomaly detection, diffusion index-based forecasting, first-order energy...
A predictive linear regression model was developed to predict the microinverter internal temperature operating under real-world conditions on dual-axis trackers. The is a function of statistically significant variables: ambient temperature, photovoltaic (PV) module irradiance and AC power data. Time-series environmental, data were analyzed in statistical analytical approach identify variables. adjusted r-squared value 0.9793. dominant contributor PV backsheet temperature.
Time-series insolation, environmental, thermal and power data were analyzed in a statistical analytical approach to identify the performance of microinverters on dual-axis trackers under real-world operating conditions. This study 24 connected 8 different brands photovoltaic (PV) modules from July through October 2013 at Solar Durability Lifetime Extension (SDLE) SunFarm Case Western Reserve University. Exploratory analysis shows that microinverter's temperature is strongly correlated with...