Increasing self-production of electricity at home has a couple of benefits.

• Enhancing decarboniztion (only 29% of electricity from grid was produced from renewable power sources (PGE, 2019)).
• Increasing independency of power production.

My first solar system produced 3803 kWh per year (an average of 2015 - 2020) ¹. Switching a gas-powered car to an electric car in 2019 increased electricity comsumption 2.5 times (from (3938 to 10342 kWh). The last year (2020) I started second solar system project for my house to increase production of electricity at home. I will describe detailed electricity data analysis on my house after expanding solar system later post. In this post, I will summarized timelines of the secondsolar system installation, which took time than I expected possibly due to series of avoidable mistakes. In view of data analysis,Gannt chart is useful visualization tool of timeline. lubridate R package is used for handling date data and lares R packages is used for making Gannt chart.

devtools::install_github("laresbernardo/lares")

library(tidyverse);library(lubridate);library(lares)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

# since today() is ymd, use ymd for all
today <- as.character(today())
# 
solar.inst <- tribble(~type,~event,~start,~end,
"CA’s county\n risk levels\n in my County","purple","2020-11-17",   "2021-02-23",
"CA’s county\n risk levels\n in my County","red","2021-02-23","2021-03-23",
"CA’s county\n risk levels\n in my County","orange", "2021-03-23",today,
"solar system","reviewing & signing DocSign","2020-11-03","2020-11-07",
"solar system","design approval","2020-11-19","2020-11-20",
"solar system","contact from designer","2020-11-17","2020-11-18",
"solar system","installation (1st attempt) [^2]","2020-11-28","2020-11-29",
"solar system","installation (2nd attempt)","2020-12-18","2020-12-19",
"solar system","1st payment, $6912", "2020-12-21","2020-12-22",
"solar system","city inspection (1st)","2021-01-20","2021-01-21",
"solar system","city inspection (2nd)","2021-02-05","2021-02-06",
"solar system","2nd payment, $1728","2021-02-09","2021-02-10",
"solar system","Application of PGE permission by Ilum solar","2021-02-11","2021-02-12",
"solar system","Approval by PGE","2021-02-24","2021-02-25",
"solar system","Obtaining info for access to solarEdge inverter from mySolarEdge app.","2021-02-25","2021-02-26",
"solar system","Ilumn solar technitian visit [^3]","2021-03-02","2021-03-03",
"solar system","Ilumn solar technitian visit [^4]","2021-03-09","2021-03-10",
"solar system","Ilumn solar technitian visit [^5]","2021-03-11","2021-03-12",
"solar system","Ilumn solar technitian visit [^6]","2021-03-16","2021-03-17",
"solar system","confirmation of electricity production [^7]","2021-03-16","2021-03-20") 

# format date as Date
solar.inst <- solar.inst %>% mutate(start2 = ymd(start),end2=ymd(end))
# sort by start2 date
solar.inst <- solar.inst %>% arrange(start2)
# 
p <- plot_timeline(event = solar.inst$event, 
              start = solar.inst$start2, 
              end = solar.inst$end2, 
              title = "Timeline of our 2nd solar system installation",
              subtitle = "",
              # Simple trick to re-arrange the grids
              group = factor(solar.inst$type, levels = c("CA’s county\n risk levels\n in my County", "solar system")))
# plot
p + theme(axis.text.x=element_text(angle=90))

## Warning: Removed 40 rows containing missing values (geom_label).