dyn (version 0.2-8.1)

baltimore: Baltimore energy data

Description

Heating costs for a home in Baltimore.

Usage

data(baltimore)

format

The data set is a zoo series with a "Date" class time index and 6 numeric numeric columns. ll{ start date of start of billing period therms integer number of therms used in this billing period gas Total cost of gas (including delivery and commodity charges) for natural gas KWHs integer number of KWH used in this billing period elect Total cost of electricity (including delivery and commodity charges) temp average daily outdoor temperature in degrees Fahrenheit, as printed on the bill days number of days in billing period. }

source

Zembower, Kevin, https://www.stat.math.ethz.ch/pipermail/r-help/2006-October/115499.html and correction of one documentation item in https://www.stat.math.ethz.ch/pipermail/r-help/2006-October/115632.html.

Details

Heating system is a 10-15 year old natural gas steam boiler supplying iron radiators. Hot water heater, clothes dryer and stove and oven are also natural gas. Air conditioning is by various numbers of window units. If surface area of house is desired, I can add this at a later time. Some interesting points in time: ll{ 22-Apr-04 Date when house was upgraded 2 failed, older storm windows to more modern ones. 1-Sep-04 Date when house was upgraded 4 failed, older storm windows to more modern ones. Interesting question: Did upgrading the windows significantly change the heat loss? last week of July 1999 Spouse moved in; both adults absent during the work day, setback thermostat used. Interesting question: Is there a discernable difference in the energy costs for heating between a single person and a couple? What's the heating "cost" of adding a spouse or roommate? 18-Dec-2005 Brought home son; spouse and son home during the day, setback thermostat no longer used. Interesting question: What's the "cost" of adding a child? }

Examples

library(lattice)
data(baltimore)
xyplot(baltimore)
cor(baltimore)
xyplot(elect + gas ~ temp, 
	data = as.data.frame(baltimore), pch = 20, auto.key = TRUE)