Temporal Solar Forecasting

require(ggmap)
station.metadata <- read.csv("/Users/carlos/Google Drive/Research/SolarForecasting/datasets/mesowest/carlos/mesowest_scraper/station_metadata.csv")
oahu.bb <- c(left=-158.3,bottom=21.35,right=-157.7,top=21.7)
#oahu.bb <- make_bbox(lon=LONGITUDE, lat=LATITUDE, data=station.metadata, f = 0.05)
map <- get_map(location=oahu.bb,zoom=10)
ggmap(map) + geom_point(
        aes(x=LONGITUDE, y=LATITUDE, show_guide = TRUE, colour=ELEVATION), 
        data=station.metadata, alpha=.8, na.rm = T)  + 
        scale_color_gradient(low="beige", high="blue")

source("/Users/carlos/Google Drive/Research/SolarForecasting/datasets/mesowest/carlos/solarf/vignettes/spatial_prob_model.R")
library(solarf)

## 
## Attaching package: 'solarf'
## 
## The following objects are masked _by_ '.GlobalEnv':
## 
##     error.absolute.centroid, station.predict.daily.cluster

vec <- function(df,wv,start=NA,end=NA){ 
    #df <- df[df$YEAR >= 2003,]
    df<- station.vectorize(df,wv)[,c(1:3,12:21)] 
    df <- add.missing.numeric.profiles(df,start,end)
    df$mean <- rowMeans(df)   
    return (df)
}
model <- list()
model[["data"]][["SCBH1"]] <- read.csv("/Users/carlos/Google Drive/Research/SolarForecasting/datasets/mesowest/data/SCBH1.csv")
model[["data"]][["KFWH1"]] <- read.csv("/Users/carlos/Google Drive/Research/SolarForecasting/datasets/mesowest/data/KFWH1.csv")



v <- vec(model[["data"]][["KFWH1"]],"SOLR")

## Warning: package 'zoo' was built under R version 3.1.3

## 
## Attaching package: 'zoo'
## 
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Warning: package 'plyr' was built under R version 3.1.3

vf <- data.frame(time=as.Date(paste0(v$YEAR,"/",v$MON,"/",v$DAY)),mean=v$mean)
vf2 <- vf
#vf2$mean <- vf$mean - mean(vf$mean,na.rm=TRUE)

library(googleVis)

## Warning: package 'googleVis' was built under R version 3.1.3

## 
## Welcome to googleVis version 0.5.10
## 
## Please read the Google API Terms of Use
## before you start using the package:
## https://developers.google.com/terms/
## 
## Note, the plot method of googleVis will by default use
## the standard browser to display its output.
## 
## See the googleVis package vignettes for more details,
## or visit http://github.com/mages/googleVis.
## 
## To suppress this message use:
## suppressPackageStartupMessages(library(googleVis))

# op <- options(gvis.plot.tag = 'chart') op <- options(gvis.plot.tag =
# 'NULL')
library(knitr)

## Warning: package 'knitr' was built under R version 3.1.3

library(markdown)

## Warning: package 'markdown' was built under R version 3.1.3

vis <- gvisCalendar(data = vf2, datevar = "time", numvar = "mean", options = list(title = paste("Solar Mean in", 
    names(model[["data"]])[[2]]), calendar = "{cellSize:10,\n                                 yearLabel:{fontSize:20,color:'#444444'},\n                                 focusedCellColor:{stroke:'red'}}", 
    width = 590, height = 1320))

plot(vis, "chart")

library(lattice)

## Warning: package 'lattice' was built under R version 3.1.3

library(chron)
year <- as.numeric(sapply(strsplit(as.character(vf$time),split="-"),"[[",1))
source("http://blog.revolutionanalytics.com/downloads/calendarHeat.R")
vf4 <- vf[year > 2009,]
calendarHeat(dates=vf4$time, values=vf4$mean, varname="mean")

## Loading required package: grid

colnames(vf4)[1] <- "date"
require("openair")

## Loading required package: openair

## Warning: package 'openair' was built under R version 3.1.3

## Loading required package: lazyeval
## Loading required package: dplyr

## Warning: package 'dplyr' was built under R version 3.1.3

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## 
## Loading required package: maps

calendarPlot(vf4, pollutant = "mean", year = 2010, annotate = "date")