TEAM CACHE MONEY:
SOLAR INSOLATION FORECASTING
PRELIMINARY DESIGN REVIEW
B. DiRenzo, L. Hager, A. Fruge,
M. Dickerson, C. Duclos, N. Frank,
T. Furlong
OUTLINE
Objectives
 Background
 System Overview
 Primary Use Case
 High Level Functional Decomposition
 Risks and Contingencies
 Division of Labor
 Budget
 Milestones

B. DiRenzo
OBJECTIVES
Create an inexpensive, real-time, and accurate
solar insolation forecasting map.
 Targeted for use by power companies to
efficiently stabilize the power grid with solar
generated energy.
 Make large scale use of PV arrays more feasible
and reliable.

B. DiRenzo
BACKGROUND
Up to 40% of power can be supplied by solar
energy (eg Hawaii).
 Cloud cover creates major drop-off in energy
production.
 Leads to grid being unstable.
 Similar methods exist for wind energy.
 Unreliability limits use of on-grid PV arrays.

B. DiRenzo
POWER OUTPUT (W) FROM A PV ARRAY
ON A CLOUDY DAY VS. A SUNNY DAY
8000
7000
6000
5000
Cloudy Day
4000
Sunny Day
3000
2000
1000
0
6:00
8:24
10:48
13:12
15:36
18:00
*PV data provided by Professor Gasiewski
L. Hager
SYSTEM OVERVIEW

Remote smart-phone sensors


On-grid PV array power sensors


Transmits photos of cloud coverage
Transmits real-time power measurements
Localized server
Parses data and computes forecast using cloud
motion vectors in real-time
 Generates insolation forecast map with error bars

L. Hager
PRIMARY USE CASE
Power Engineer seeks to use the final GUI
application to make smart decisions about how
the power company will generate power in the
near future.
 Engineer may also want to look back on past
predictions to compare with actual solar
statistics.

T. Furlong
HIGH LEVEL DESIGN
T. Furlong
FUNCTIONAL DECOMPOSITION LEVEL 0
T. Furlong
FUNCTIONAL DECOMPOSITION: LEVEL 1
T. Furlong
LEVEL 2 SUB-SYSTEM: REMOTE SENSOR
Camera
Charge Controller
Battery Bank
Android
Timing
Application
To Server
via 3G
A. Fruge
LEVEL 2 SUB-SYSTEM: ON-GRID PV
SENSOR
A. Fruge
LEVEL 2 SUB-SYSTEM: SERVER
Inputs cloud
images
Receives Data
Cloud images
Residential
power
measurement
s
Network:
Receives
data from
sensors and
inputs to
appropriate
location
Inputs power
measurements
User inputs, then GUI
displays to user
Image
Processor:
GUI:
Receives user
input and
displays
appropriate
forecast map
Determines cloud
motion vectors
and sends to
forecaster
Inputs motion vectors
Inputs forecasting map
Forecaster:
Creates forecast
map every
minute, using
data received and
updates database
Map
Creator:
Inputs forecast data
Receives
forecasting data
and outputs
forecasting map
to GUI
Database:
Saves forecast
map and inputs
appropriate
forecast data to
map creator
C. Duclos
Inputs forecast data
Inputs requested map
data
C. Duclos
RISKS AND CONTINGENCIES
• Due to lack of sunlight, Remote Sensor may lose
power.
– Battery is chosen to be large enough to power the
sensor for up to 4 days with no sunlight.
• Due to lack of network coverage, data from
Remote Sensor may not be transmitted in real
time or at all.
– Program will be able to compensate for an incomplete
data set through the error calculations.
M. Dickerson
RISKS AND CONTINGENCIES CONTINUED
• Camera lens may have obstructions preventing
pictures from obtaining accurate cloud data.
– Software will be able to tell the difference between
obstructions and clouds.
– Protective casing will mitigate the amount of debris that
will be able to cover the lens.
• Direct sunlight may cause CCD array to be
burned, and therefore lose image quality or
create “blind spots” on images.
– Protective lens filter will ensure minimal damage to the
CCD array.
M. Dickerson
DIVISION OF LABOR
Job
Owner(s)
Remote smartphone
sensor
B. DiRenzo, A. Fruge
On-Grid PV Array
L. Hager, N. Frank
Localized Server
C.Duclos, T. Furlong
Power Systems
M. Dickerson
Chief Financial Officer
L. Hager
N. Frank
BUDGET
N. Frank
Subtotal
4900
N. Frank
FIRST SEMESTER MILESTONES
N. Frank
SECOND SEMESTER MILESTONES
N. Frank
THE END