dominionPowerToInfluxDB.py

#!/usr/bin/python2.7

''' This script will download the detailed energy usage from dominion power and insert it into InfluxDB which you can then visualize with Grafana
https://www.dominionenergy.com/company/electric-projects/smart-meters/detailed-energy-usage
'''

import mechanize
import cookielib
import csv
import pytz
from influxdb import InfluxDBClient
from dateutil import parser

def getCSV():
   # Browser
   br = mechanize.Browser()

   # Cookie Jar
   cj = cookielib.LWPCookieJar()
   br.set_cookiejar(cj)

   # Browser options
   br.set_handle_equiv(True)
   br.set_handle_redirect(True)
   br.set_handle_referer(True)
   br.set_handle_robots(True)
   br.set_handle_refresh(mechanize._http.HTTPRefreshProcessor(), max_time=1)

   # for debug
   br.set_debug_http(False)
   br.set_debug_redirects(False)
   br.set_debug_responses(False)

   # User-Agent
   br.addheaders = [('User-agent', 'Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.1) Gecko/2008071615 Fedora/3.0.1-1.fc9 Firefox/3.0.1')]

   # Open site
   r = br.open('https://mya.dominionenergy.com/Usage/DailyIntervalData')

   # Select the first (index zero) form
   br.select_form(nr=0)

   # User credentials
   br.form['USER'] = 'your_username'
   br.form['PASSWORD'] = 'your_password'

   # Login
   br.submit()

   # find the latest csv for kilowatts
   for l in br.links(url_regex='ViewDailyIntervalData'):
    if 'csv(KW)' in l.text:
	baseurl = 'https://mya.dominionenergy.com'
   	br.retrieve(baseurl + l.url, '/tmp/history-kw.csv')[0]


def send_to_influx(ts, value):
   # influxdb settings
   host = 'influxdb.local'
   port = 8086
   user = 'root'
   password = 'root'
   dbname = 'dominion'
   dbuser = 'smly'
   dbuser_password = 'my_secret_password'

   client = InfluxDBClient(host, port, user, password, dbname)
   json_body = [
          {
              "measurement": "powerUsage",
              "tags": {
                  "deviceName": "utility",
              },
	      "time": ts,
              "fields": { "value": float(value) }
          }
      ]

   client.write_points(json_body)


def doMetrics():
   utc = pytz.utc
   local_tz = pytz.timezone('America/New_York')
   columns = { 3: '00:30',
   	       4: '01:00',
	       5: '01:30',
	       6: '02:00',
	       7: '02:30',
	       8: '03:00',
	       9: '03:30',
	       10: '04:00',
	       11: '04:30',
	       12: '05:00',
	       13: '05:30',
	       14: '06:00',
	       15: '06:30',
	       16: '07:00',
	       17: '07:30',
	       18: '08:00',
	       19: '08:30',
	       20: '09:00',
	       21: '09:30',
	       22: '10:00',
	       23: '10:30',
	       24: '11:00',
	       25: '11:30',
	       26: '12:00',
	       27: '12:30',
	       28: '13:00',
	       29: '13:30',
	       30: '14:00',
	       31: '14:30',
	       32: '15:00',
	       33: '15:30',
	       34: '16:00',
	       35: '16:30',
	       36: '17:00',
	       37: '17:30',
	       38: '18:00',
	       39: '18:30',
	       40: '19:00',
	       41: '19:30',
	       42: '20:00',
	       43: '20:30',
	       44: '21:00',
	       45: '21:30',
	       46: '22:00',
	       47: '22:30',
	       48: '23:00',
	       49: '23:30',
   	       50: '00:00'
   }


   counter = 3

   with open('/tmp/history-kw.csv', 'rb') as csvfile:
    reader = csv.reader(csvfile, delimiter=',', quotechar='|')
    for row in reader:
   	while counter < 51:
           dt = parser.parse(row[2] + ' ' + columns[counter])
	   warp = local_tz.localize(dt)
	   time = warp.astimezone(utc).strftime('%Y-%m-%dT%H:%M:%SZ')
	   send_to_influx(time, row[counter])
	   counter += 1
        counter = 3


### BEGIN MAIN PROGRAM
getCSV()
doMetrics()