Occupancy Prediction

In this post, I will try to guess the hours in which property is occupied by tenants. The prediction will be performed through the use of different arbitrary thresholds in electricity consumption data. The main aim is to find time intervals, which tenants are at home, with high accuracy. For privacy issues, the owner of the data is not represented.

11 Jan 2013, Friday.

Power-consumption hours: 00.00 - 01.00, 05.00 - 08.00, 17.00 - 24.00

12 Jan 2013, Saturday.

Power-consumption hours: 00.00 - 01.00 , 05.00 - 08.00, 11.00 - 14.00, 20.00 - 21.00, 23.00 - 24.00

13 Jan 2013, Sunday.

Power-consumption hours: 00.00 - 02.00 , 08.00 - 13.00, 20.00 - 22.00

14 Jan 2013, Monday.

Power-consumption hours: 06.00 - 22.00

15 Jan 2013, Tuesday.

Power-consumption hours: 00.00 - 01.00 , 06.00 - 08.00, 18.00 - 24.00

16 Jan 2013, Wednesday.

Power-consumption hours: 00.00 - 01.00 , 06.00 - 08.00, 17.00 - 24.00

17 Jan 2013, Thursday.

Power-consumption hours: 00.00 - 01.00 , 05.00 - 08.00, 17.00 - 24.00

Please note that the hours stated above represent the time intervals in which tenants consume observable energy. Hence, we might assume that at least one occupant is at home during these time intervals. 

MatPlotLib

MatPlotLib is a plotting library which is created by John Hunter. It offers an useful object-oriented API for Python programmers. For further information please see the link : http://matplotlib.org/

A sample graph which shows the historical electricity use in a day. 

Team Energy

I completed the implementation of TeamEnergy which is a web application allowing multiple users to coordinate their energy consumption activities. The project is uploaded to the bitbucket which provides unlimited private repositories for online users. The steps followed for the process of uploading can be found in the link below.

https://confluence.atlassian.com/display/BITBUCKET/Set+up+Git+and+Mercurial

The articles I read are as follows:

- A Large-Scale Study on Predicting and Contextualizing Building Energy Usage 

In this paper, a data-driven approach is represented to model energy consumption in buildings. The monthly electricity and gass bills are used as data which is collected by a utility for several years. Both parametric and non-parametric learning methods are utilised to model energy consumptions related to the features of the buildings. Based on these models, two end-user system is offered. One is for utilities or authorised institutions, and it visualises energy consumption for each unit. The other system enables consumers to enter their own data like the features of the building and energy usage, and the system, in return, provides a comparision of similar buildings. 

* In order to compare energy consumptions of buildings, electricity and gas usages are combined to obtain equivalent units through the use of conversation factor (1/3) 29.3 therms/kwh of the US. We can use this conversion in our home energy management system.

- The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes

Basically, this paper presents a smart thermostat which adjusts set points according to occupancy patterns that are generated through several sensors.  

Finally, I finished reading the book "Plans and Situated Actions" by Suchman.