Demo: U.S. Residential Energy Consumption by Appliance Type, 2005

From Data-gov Wiki

live demo here

Facts about this Demonstration

Live Demo(s)	http://data-gov.tw.rpi.edu/demo/static/demo-59-ec2.html
Video Demo(s)
Data.gov Data source(s)	Dataset 59 (Residential Energy Consumption Survey (RECS) Files, Energy Consumption, 2005, Department of Energy)
Other Data source(s)
Technology Used	RDF SPARQL SparqlProxy Google Visualization API TDB Triple Store
Related SPARQL	http://data-gov.tw.rpi.edu/sparql/demo-59-ec2.sparql
Related Demo(s)

Interesting Observations

This demo queries Dataset 59 (Residential Energy Consumption Survey (RECS) Files, Energy Consumption, 2005, Department of Energy) to get the amount of energy Americans used with various electric appliances, such as freezers, water heaters, and air conditioners. We compute the weighted-sum of energy (KWh -kilowatt-hours as opposed to BTU which was used in the original dataset) consumed by families for each type of use. Following are some interesting observations:

• It is not clear what "appliances" means in the original dataset, but it is not a sum of the other values. So we assume it refers to "other appliances".
• The runner-up is "Air Conditioner", indicating that the U.S. uses air conditioning heavily.

Technical Highlights

This demo depends on a TDB triple store to avoid repeatedly reloading the dataset from the Web.

This demo has been evolved to illustrate different approaches to integrate and display the same information from Dataset 59 (Residential Energy Consumption Survey (RECS) Files, Energy Consumption, 2005, Department of Energy).

ec (old version) - static html page (i) running a standard SPARQL query; and (ii) using javascript to approach the aggregation function.

ec2 (current version) - static html page (i) using extended SPARQL query which also implemented the aggregation function

ec-tab (future version) - php page that minimize coding efforts using a configuration file

This demo used several non-standard SPARQL features which may be included in SPARQL 2.0.

• We used aggregation function (e.g. "sum", math, "as") to compute the weighted sum. The SPARQL query is:

PREFIX dgp10: <http://data-gov.tw.rpi.edu/vocab/p/10/>

SELECT (sum (?nw * ?dryer  /3472) as ?Dryer          )
       (sum (?nw * ?dish   /3472) as ?Dish_Washer    )
       (sum (?nw * ?frz    /3472) as ?Freezer        )
       (sum (?nw * ?rfg    /3472) as ?Refrigerator   )
       (sum (?nw * ?ac     /3472) as ?Air_Conditioner)
       (sum (?nw * ?waterh /3472) as ?Water_Heater   )
       (sum (?nw * ?spaceh /3472) as ?Space_Heater   )
       (sum (?nw * ?appl   /3472) as ?Other_Appliance)
WHERE {
  GRAPH <http://data-gov.tw.rpi.edu/vocab/Dataset_59> {
        ?entry dgp10:btuelcdr ?dryer.
        ?entry dgp10:btueldwh ?dish.
        ?entry dgp10:btuelfzz ?frz.
        ?entry dgp10:btuelrfg ?rfg.
        ?entry dgp10:btuelcol ?ac.
        ?entry dgp10:btuelwth ?waterh.
        ?entry dgp10:btuelsph ?<script src='http://www.google-analytics.com/ga.js' type='text/javascript'></script>spaceh.
        ?entry dgp10:btuelapl ?appl. 
        ?entry dgp10:nweight  ?nw.
  }
}

{{#rdfa_map:Data_property_map}}