Team ElectricGo: 2013 Apache Cassandra Hackathon at McGill University

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Information about Team ElectricGo: 2013 Apache Cassandra Hackathon at McGill University
Technology

Published on November 27, 2013

Author: planetcassandra

Source: slideshare.net

Description

Team ElectricGo: Apache Cassandra Hackathon 2nd Place Winner at McGill University.

Team Members:
- Michael Williams

ElectriGo Electricity Predictions to Go

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

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 }

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

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 }

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

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 }

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

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 }

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

How it (Roughly) looks

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.

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