Data Analysis Solar Panels
•Download as PPTX, PDF•
1 like•314 views
This document discusses analyzing and presenting big data from renewable energy systems. It includes charts showing average temperature over time and produced kilowatt hours over time from solar tracking systems. Key performance indicators measure kilowatt hours over time to evaluate progress toward the objective of meeting power needs through increasing the number of Yingli solar tracking systems over five years. Analysis indicates Yingli trackers are most valuable and open systems are most reliable, and correct interpretation of data through analysis can lead to valuable results.
Report
Share
Report
Share
Recommended
In this project the group members will play with daily rainfall data collected in Gulf coast (535stations in total) from 1949 to 2017. The purposes of this exercise are to:
1) to give students an idea of a typical example of a climate data set (spatio-temporal data) and someassociated scientific questions (e.g. how rainfall extremes vary in space and time and how that mightbe affected by other things like greenhouse gases or temperatures).
2) to get students familiar with data analysis using R including data manipulation, data visualization, and data summary.
3) to introduce some statistical methods (e.g. time series analysis, spatial statistics, extreme value analysis) to analyze this kind of data to "answer" (perform statistical inference) the questions of interest.
Group members: Lin Ge, Jianan Jang, Jessica Robinson, Erin Song, Seth Temple, Adam Wu
Undergraduate Modeling Workshop - Southeastern US Rainfall Working Group Fina...
Undergraduate Modeling Workshop - Southeastern US Rainfall Working Group Fina...The Statistical and Applied Mathematical Sciences Institute
Recommended
In this project the group members will play with daily rainfall data collected in Gulf coast (535stations in total) from 1949 to 2017. The purposes of this exercise are to:
1) to give students an idea of a typical example of a climate data set (spatio-temporal data) and someassociated scientific questions (e.g. how rainfall extremes vary in space and time and how that mightbe affected by other things like greenhouse gases or temperatures).
2) to get students familiar with data analysis using R including data manipulation, data visualization, and data summary.
3) to introduce some statistical methods (e.g. time series analysis, spatial statistics, extreme value analysis) to analyze this kind of data to "answer" (perform statistical inference) the questions of interest.
Group members: Lin Ge, Jianan Jang, Jessica Robinson, Erin Song, Seth Temple, Adam Wu
Undergraduate Modeling Workshop - Southeastern US Rainfall Working Group Fina...
Undergraduate Modeling Workshop - Southeastern US Rainfall Working Group Fina...The Statistical and Applied Mathematical Sciences Institute
8th PVPMC Workshop, May 9-10 201702 sandia pv modeling conference 9 may2017
02 sandia pv modeling conference 9 may2017Sandia National Laboratories: Energy & Climate: Renewables
Fine particulate matter (PM2.5) is a mixture of air pollutants that, at a high concentration level, has adverse effects on human health. An interesting statistics problem is to estimate these pollutant exposures for the entire US, such estimates can be used to inform policy and decision making. During the workshop, we will work on two major source of air quality data that are used by the EPA to estimate pollutant exposures, including monitoring data and the Community Multiscale Air Quality (CMAQ) model. The monitoring stations provide fairly accurate measurements of the pollutants; however, they are sparse in space and take
measurements at a coarse time resolution, typically 1-in-3 or 1-in-6 days. On the other hand, the CMAQ model provides daily concentration levels of each component with complete spatial coverage on a grid; these model outputs, however, need to be evaluated and calibrated to the monitoring data. We will explore these air quality data for the summer of 2011 and brainstorm on statistical models to estimate air pollutant exposures.
Group members: Meixi Chen, Vincent Gonzales, Alan Ji, Chandni Malhotra, Hongyu Mao, Sharon SungUndergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...
Undergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...The Statistical and Applied Mathematical Sciences Institute
More Related Content
What's hot
8th PVPMC Workshop, May 9-10 201702 sandia pv modeling conference 9 may2017
02 sandia pv modeling conference 9 may2017Sandia National Laboratories: Energy & Climate: Renewables
Fine particulate matter (PM2.5) is a mixture of air pollutants that, at a high concentration level, has adverse effects on human health. An interesting statistics problem is to estimate these pollutant exposures for the entire US, such estimates can be used to inform policy and decision making. During the workshop, we will work on two major source of air quality data that are used by the EPA to estimate pollutant exposures, including monitoring data and the Community Multiscale Air Quality (CMAQ) model. The monitoring stations provide fairly accurate measurements of the pollutants; however, they are sparse in space and take
measurements at a coarse time resolution, typically 1-in-3 or 1-in-6 days. On the other hand, the CMAQ model provides daily concentration levels of each component with complete spatial coverage on a grid; these model outputs, however, need to be evaluated and calibrated to the monitoring data. We will explore these air quality data for the summer of 2011 and brainstorm on statistical models to estimate air pollutant exposures.
Group members: Meixi Chen, Vincent Gonzales, Alan Ji, Chandni Malhotra, Hongyu Mao, Sharon SungUndergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...
Undergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...The Statistical and Applied Mathematical Sciences Institute
What's hot (13)
Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...
Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...
Big data, Cloud, and the NOAA CRADA at The Climate Corporation
Big data, Cloud, and the NOAA CRADA at The Climate Corporation
Undergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...
Undergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...
Large Scale Stream Analytics using a Resource-constrained Edge
Large Scale Stream Analytics using a Resource-constrained Edge
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
Day 3 ben harrison, met office, arrcc-carissa workshop
Day 3 ben harrison, met office, arrcc-carissa workshop
Recently uploaded
Recently uploaded (20)
Webinar One View, Multiple Systems No-Code Integration of Salesforce and ERPs
Webinar One View, Multiple Systems No-Code Integration of Salesforce and ERPs
2024-05-14 - Tableau User Group - TC24 Hot Topics - Tableau Pulse and Einstei...
2024-05-14 - Tableau User Group - TC24 Hot Topics - Tableau Pulse and Einstei...
How can I successfully sell my pi coins in Philippines?
How can I successfully sell my pi coins in Philippines?
Supply chain analytics to combat the effects of Ukraine-Russia-conflict
Supply chain analytics to combat the effects of Ukraine-Russia-conflict
Innovative Methods in Media and Communication Research by Sebastian Kubitschk...
Innovative Methods in Media and Communication Research by Sebastian Kubitschk...
Business update Q1 2024 Lar España Real Estate SOCIMI
Business update Q1 2024 Lar España Real Estate SOCIMI
Tabula.io Cheatsheet: automate your data workflows
Tabula.io Cheatsheet: automate your data workflows
Using PDB Relocation to Move a Single PDB to Another Existing CDB
Using PDB Relocation to Move a Single PDB to Another Existing CDB
Data Analysis Solar Panels
- 1. Data Analysis DASHBOARD SLIDE CAST RAPPORT
- 2. Context Howest Kortrijk NMCT BPM Interpreting, analysing and presenting big data
- 3. Data
- 4. Average Temperature Over Time
- 5. Produced kWh Over Time
- 6. Pivot Table
- 7. KPI Specific Objective: Be in dependable for power needs Measure: Measure kWh over time Attainable: By increasing number of Yingli tracking solar systems Time-Frame: 5 years
- 8. Interpretation Yingli Trackers Most Valuable Yingli Open Systems Most Reliable
- 9. Personal Conclusion Data Is vital Analysis is everything Correct interpretation can lead to amazing results