Drew Hanover's 2015-2016 research summary included three main projects: 1) Monitoring the thermal response and energy consumption of a building using wireless sensors and software. 2) Learning about resistor-capacitor building modeling and creating a mockup model in MATLAB. 3) Developing a mathematical model to predict solar panel output given inputs like irradiation and temperature, validating the model using experimental data.

1. Research Summary
Drew Hanover
2015-2016

2. Building Performance
• Learned how to monitor thermal response of a building over a given
period of time using Wi-Fi thermal sensors
• Installed sensors in AERB for data acquisition
• Researched how to monitor energy consumption of a building by
using TED Footprints software

3. = SENSOR LOCATION
Outside
Probe

4. • Compiled AERB thermal and electrical data into one
main Excel file for ease of use and accessibility in the
future

5. Building Performance
• Learned about RC building modeling
• Practiced creating a mockup RC model in MATLAB
• Read and studied literature on MPC modeling
• Make the Duck Fly!

6. Solar Panel Research
• Asked to develop a mathematical model that predicts panel output
given various inputs
• After reading many papers, I developed my first model in Simulink

7. Solar Panel Research
• The Simulink model helped us to understand how a panel would
perform when one input was held constant and another was varied

8. Solar Panel Research
• Needed to change the model in order to accurately predict power
output given a varying irradiation and temperature vector as input
• The model was reworked following NREL’s Detailed Performance
Model for Photovoltaic Systems

9. Solar Panel Research
• Worked with Abhilash Kantamneni in gathering experimental output data
from KRC
• Researched NREL’s System Advisor Model which proved to be an extremely
useful tool in developing our model
• Panel parameters
• Irradiation and temperature data is taken from NREL’s Physical Solar Model (PSM)
“PSM uses a two-step process where cloud properties are retrieved using the adapted
PATMOS-X model, which are then used as inputs to REST2 for clear sky and FARMS for
cloudy sky radiation calculations. REST2 calculates both DNI and GHI. FARMS calculates
GHI, and the DISC model is then used to calculate DNI. Aerosol properties are estimated
using MODIS, MISR, and AERONET products. Water vapor is obtained from NASA
MERRA. Additional meteorological parameters are also derived from MERRA.”

10. Equations

11. Validation
• Model was validated for winter and summer months using KRC data

12. Validation
• Results are good, however we are limited to the accuracy of the PSM
irradiation and temperature predictions in Houghton
• If the conditions are not identical to the KRC, the output will be different

13. Validation
• Model accurately predicts panel dynamics and can be implemented
into MPC control strategy

14. Sensitivity Analysis
• Need to understand ΔPower for a percent change in Irradiation or
Temperature
• Meysam and I almost have this problem solved

15. Battery Map
• Using Jeremy Dobb’s battery model discussed in his MSc thesis we
were able to implement the PV model as a charge source for the LG
battery

16. Paper
• Upon completion of the uncertainty and sensitivity analysis, Meysam
and I will begin writing the paper
• I have compiled all of our sources regarding PV modeling and wrote
the LaTex code for the equations used

17. Questions?
Thank you!