ElectriGo is a service that predicts future power usage based on past usage data, weather records, and forecasts. It stores user, tracker, weather, and record data in databases. Trackers associated with users can receive power usage predictions to help utilities better manage their resources. The business model involves selling the prediction software or service to utilities to help lower costs by anticipating peak demand.

1. ElectriGo
Electricity Predictions to Go

2. The Goal
Predict the future power usage based off past
power usage, past weather, and future weather
forecasts

3. The Data - Users
CREATE TABLE users (
username text,
addtracker boolean,
adduser boolean,
password text,
salt text,
PRIMARY KEY (username)
)
type User struct {
Username
string
CanAddUser bool
CanAddTracker bool
Password
string
salt
string
}

4. User Data Example
username | addtracker | adduser | password
| salt
----------+------------+---------+--------------------------+-----bob |
True | True | VWRESCEASA3453ASDF323R3Q | T5DW

5. The Data - Trackers
CREATE TABLE trackers (
api_key text,
id bigint,
owner text,
period bigint,
PRIMARY KEY ((api_key, id))
)
CREATE INDEX trackers_owner_idx
ON trackers (owner);
type Tracker struct {
API_String string
ID
int
Period int
Owner
string
Predictor *predictions.Predictor
stop
chan bool
}

6. Tracker Data
api_key
| id | owner | period
----------------------------------+-------+-------+-------B25ECB703CD25A1423DC2B1CF8E6F008 | 50578 | bob |
60

7. The Data - Weather
CREATE TABLE weatherrecords (
station uuid,
time timestamp,
cloudcover double,
humidity double,
pop double,
temperature double,
windspeed double,
PRIMARY KEY (station, time)
)
type WeatherRecord struct {
Time
time.Time
Humidity float64
Temperature float64
WindSpeed float64
PoP
float64
CloudCover float64
}

8. Weather Data Example
station
| time
| cloudcover | humidity | pop | temperature | windspeed
--------------------------------------+--------------------------+------------+----------+-----+-------------+----------d4f560cf-6a9c-4ec0-8af1-a37c5e664ff9 | 2013-11-23 02:00:00-0500 |
0.6 | 0.85 | 1 | -26.122 | 2.6062
d4f560cf-6a9c-4ec0-8af1-a37c5e664ff9 | 2013-11-23 02:15:00-0500 | 0.6225 | 0.8475 | 1 |
-26.11 | 2.6018
d4f560cf-6a9c-4ec0-8af1-a37c5e664ff9 | 2013-11-23 02:30:00-0500 |
0.645 | 0.845 | 1 | -26.097 | 2.5973

9. The Data - Record
CREATE TABLE records (
api_key text,
id bigint,
time timestamp,
prediction boolean,
value double,
PRIMARY KEY ((api_key, id), time,
prediction)
)
type Record struct {
Time
time.Time
Value
float64
Tracker *Tracker
Prediction bool
Weather *WeatherRecord
}

10. Record Data Example
api_key
| id | time
| prediction | value
----------------------------------+-------+--------------------------+------------+------B25ECB703CD25A1423DC2B1CF8E6F008 | 50578 | 2013-11-23 08:00:05-0500 |
B25ECB703CD25A1423DC2B1CF8E6F008 | 50578 | 2013-11-23 08:15:05-0500 |
B25ECB703CD25A1423DC2B1CF8E6F008 | 50578 | 2013-11-23 08:30:05-0500 |
B25ECB703CD25A1423DC2B1CF8E6F008 | 50578 | 2013-11-23 08:45:05-0500 |
False | 15843
False | 15739
False | 15844
False | 15843

11. How it (Roughly) looks

12. Why? (The Business Side)
Power usage = Cost
Cheaper short-term power sources exit
take time to start up
Turn On Backup power sources ahead of peak
load
Sell either code or service to comapnies, both
parties make money.