In cyngularities/TFSDataMonitoring: Functions automate photovoltaic data monitoring tasks.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The functions within the library are to automate photovoltaic data monitoring tasks for higher effciency work flow.

Load Library

library(TFSDataMonitoring)

Import data

Files <- lapply(list.files(system.file('extdata', package = 'TFSDataMonitoring'), 
                           full.names = TRUE), read.csv)

Functions

Data Monitoring Down Check

dm_down merges photvoltaic monitoring data with administrative information and client site status. Exported data results in a list of current down systems, their status and customer job information.

dm_down<- function(se, sma, sp, status, clients) {
  se_filter <- se %>%
    filter(Severity == "High" | Severity == "Low" | Severity == "No Data" | kWh == "0") %>%
    left_join(status, by = "PORTAL.NAME")
  sma_filter <- sma %>%
    filter(YESTERDAY == "No data" | YESTERDAY == "0") %>%
    left_join(status, by = "PORTAL.NAME")
  sp_filter <- sp %>%
    filter(Status == "Open") %>%
    left_join(status, by = "PORTAL.NAME")
  se_final <- left_join(se_filter %>% select(PORTAL.NAME, STATUS, CATEGORY),
                        clients, by = "PORTAL.NAME")
  sma_final <- left_join(sma_filter %>% select(PORTAL.NAME, STATUS, CATEGORY),
                         clients, by = "PORTAL.NAME")
  sp_final <- left_join(sp_filter %>% select(PORTAL.NAME, STATUS, CATEGORY),
                        clients, by = "PORTAL.NAME")
  se_sma_sp <- rbind(se_final, sma_final, sp_final)
  se_sma_sp <- distinct(se_sma_sp, PORTAL.NAME, .keep_all= TRUE)
  return(se_sma_sp)
}

Monthly Production Check

monthly_negative_deviation divides current monthly production with company estimates and merges with customer information and site status. Exported data is a list of under-performing sites and possible issues.

monthly_negative_deviation <- function(sma_performance,se_performance,estimates,status) {
  se_filter <- left_join(se_performance %>% select(PORTAL.NAME,MONTH),
          estimates %>% select(PORTAL.NAME,Email,
          Installation.Date,JOB.NAME,MonitorCode,AUG,PvEstMonthlyProd), by = "PORTAL.NAME")
  sma_filter <- left_join(sma_performanc %>% select(PORTAL.NAME,MONTH),
          estimates %>% select(PORTAL.NAME,Email,
          Installation.Date,JOB.NAME,MonitorCode,AUG,PvEstMonthlyProd), by = "PORTAL.NAME")
  se_sma_bind <- rbind(se_filter, sma_filter)
  se_sma_deviation <- se_sma_bind %>%
    transform(PERFORMANCE = as.numeric(MONTH)/as.numeric(AUG))%>%
    left_join(status %>% select(PORTAL.NAME,STATUS, CATEGORY), by = "PORTAL.NAME")
  negative <- filter(se_sma_deviation, PERFORMANCE < 0.85)
  distinct(negative, PORTAL.NAME, .keep_all= TRUE)
  return(negative)
}

Yearly Production Check

yearly_positive_deviation divides current yearly production with company estimates and merges with customer information and site status. Exported data is a list of sites that met or exceeded yearly estimates.

yearly_positive_deviation <- function(sma_performance,se_performance,estimates,status) {
  se_filter <- left_join(se_performance %>% select(PORTAL.NAME,YEAR),
            estimates %>% select(PORTAL.NAME,Address,City,State,Postal,Email,
            Installation.Date,JOB.NAME,MonitorCode,PvEstYearlyProd), by = "PORTAL.NAME")
  sma_filter <- left_join(sma_performance %>% select(PORTAL.NAME,YEAR),
            estimates %>% select(PORTAL.NAME,Address,City,State,Postal,Email,
            Installation.Date,JOB.NAME,MonitorCode,PvEstYearlyProd), by = "PORTAL.NAME")
  se_sma_bind <- rbind(se_filter, sma_filter)
  se_sma_deviation <- se_sma_bind %>%
    transform(PERFORMANCE = (as.numeric(YEAR)/PvEstYearlyProd)*100)%>%
  positive <- filter(se_sma_deviation, PERFORMANCE > 95.0)
  distinct(positive, PORTAL.NAME, .keep_all= TRUE)
  return(positive)
}

cyngularities/TFSDataMonitoring documentation built on March 18, 2023, 6:38 a.m.