Bayesian Heating Model
Estimates the parameters of a building's heating model.
an object of class "formula": a description of which variable holds the energy readouts and which variable holds the daily temperatures.
a data frame in which the energy and daily temperatures are to be found.
a optional constant base load, e.g. for domestic hot water preparation.
bhm assumes that the heating energy for a building has been measured
over several time periods (not necessarily of equal length). The
data frame should have one row per measurement period. The energy vector (whose
name is given on the left-hand side of the formula) will have the total energy
measured during each period. The daily temperature vector (whose name is given on
the right-hand side of the formula) will have either a vector of average daily
temperatures (when each measurement period is just one day) or a list of vectors
(when each measurement period can be an arbitrary number of days).
bhm returns an object of class "
bhm". The generic
residuals extract the usual
information from the fitted model, while
logposterior will return a function
that evaluates the log-posterior as a function of the parameters.
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set.seed(1111) # Simple, but unrealistic parameters K <- 1 tb <- 1 DHW <- 1 sigma <- 1e-2 temps <- tb + c(-2, -1, 0, 1) # With daily measurements E <- K * pmax(tb - temps, 0) + DHW + rnorm(length(temps), 0, sigma) fourDayData <- data.frame(E = E, T = temps) fourDayData ## Not run: fit <- bhm(E ~ T, fourDayData) coef(fit) resid(fit) ## End(Not run) # With two-day measurements fourTimesTwoDayData <- with(fourDayData, data.frame(E = 2 * E, T = I(lapply(T, function(x) c(x, x))))) fit2 <- bhm(E ~ T, fourTimesTwoDayData) coef(fit2) resid(fit2)
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