Building monitoring

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Revision as of 15:03, 23 September 2012 by Akseewa11 (talk | contribs) (Harvest Scripts)
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Energy Display Scripts Outline version 1.0

  1. Java scripts harvest data directly from Modbus
  2. Perl scripts collects this data and put it in the Postgress database "energy" under the table "electrical_energy"
    • electrical_energy attributes:
      • area - what building.
      • preal - kW hours for that instant.
      • date - date of harvest.
  3. A final perl script (display/production|development/js-file-gen.pl) accesses this data and generates a .png graph using the Google graph API
    • This script takes one parameter for either the production or development branches.
  4. The images generated (day, week, month, year) are put in a html frame on a page to display the data.
    • This page refreshes every 5 minutes

Energy Display Scripts Outline version 2.0

Postgres db

  • Data stored in databse 'energy' under table 'electrical_energy'
  • 'electrical energy' has the following attributes:
    • area - which building.
    • preal - kW hours for that instant.
    • date - date of harvest.

data_gen.py

  • generates a JSON file filled with data from a sql call to the database.
  • requires Python 2.5
  • file run by cron
  • Main(building_file)
    • Called from main loop.
    • Calls CreateBuildingList(), ConnectToDatabase(), GetAllData(), EncodeData(), DisconnectFromDatabase()
  • CreateBuildingList(building_file)
    • Called by Main().
    • For each building in building_file add to a python list.
    • return that list.
  • ConnectToDatabase()
    • Called by Main().
    • connects to 'energy' databse in Postgres
    • returns database connection and the current name of database.
  • GetAllData(db connection, building list)
    • Called by Main().
    • makes a python dict of all data
    • calls GetBuildingData()
    • replace unwanted dates from older buildings
    • calls EqualizeDataValues()
  • GetBuildingData(dbconnection, building)
    • called from GetAllData()
    • calls SetSQLQuery()
    • returns rows from sql call and row count
  • SetSQLQuery(building)
    • called from GetBuildingData()
    • Makes a sql call to the databse energy from the table electrical_energy
    • returns rows from sql call and row count
  • EqualizeDataValues(all_data, dates)
    • fills null values for dates of newer buildings.
    • returns all data
  • EncodeData(all_data, dates)
    • Called by Main().
    • sorts all_data by date
    • dumps data in JSON file, note: columns are organized alphabetically.
    • initializes gviz metadata
    • calls LoadData().
    • writes JSON file which JS reads from.
  • LoadData()
    • Called by EncodeData()
    • gviz api call
    • encodes data to JSON file with headers.
  • DisconnectFromDatabase()
    • Called by Main().
    • disconnects from database 'energy'.

main.js

  • non-function
    • creates several global variables.
      • whichBiuldings: an array of booleans signifying which buildings get drawn
      • buildingColors: an array of colors for each of the buttons and colors on the graph, change them here and only here.
      • startDate, endDate: range on the graph, by default they are one week ago and today respectively, if the page was reached through a permalink these get modified.
      • lineChartJSONData: this is the json file which data_gen.py makes, it's placed as a global so it doesn't have to be reloaded everytime a building is checked or unchecked.
  • flipAndRedraw
    • called when a button is pressed
    • flips values in the global whichBuildings[]
    • calls drawLineChart() provided whichBuildings has at least 1 true value.
  • flip(val)
    • takes a boolean and returns its opposite.
  • redrawVis(values[])
    • Takes an array of booleans
    • calls DrawLineChart()
  • drawLineChart(buildingArray[], colors[])
    • called when index.html loads and when RedrawVis() is called.
    • takes an array of booleans processed by RedrawVis()
    • creates a Dashboard
    • creates a data table with the data source as the json file data-gen.py makes.
    • creates a chart wrapper, this is where you can adjust basic settings about the main chart.
    • creates a control wrapper, this creates the controling bar on the bottom. This is where you can adjust the starting range for the control wrapper.
    • creates a listener for the 'statechange' event on the controlWrapper, when the control range is updated the dates are saved. When a building is unchecked and the graph redrawn, the selection stays the same.
    • creates a listener for the 'ready' event on the chartWrapper. When chartWrapper is ready (all drawn) resizeControls is called.
    • deletes columns in data table depending on the booleans that are in values[], the more columns that are deleted the better the performance.
    • binds the chart wrapper and the control wrapper together, draws the chart.
  • equalArrays(arr1, arr2)
    • checks if the contents of arr1 are equal to arr2, returns true if they are, false otherwise.
  • parseDate(urlArg[])
    • takes the array of strings from a permalink and returns them as a date object
  • parseInts(strArray[])
    • takes the array of strings from a permalink and returns them as an array of integers.
  • parseButtons()
    • Takes button arguments from the permalink and unchecks the buttons on index.html accordingly using jQuery's removeClass medthod.
  • removeIndices()
    • processes and array to return containing indices of which columns delete and therefore not draw.
  • genPermalink()
    • generates a permalink to the chart's state and which buttons are checked. The format of the permalink is (url)/?(startDate, yyyy-mm-dd)%(endDate, yyyy-mm-dd)%(buttons, 0123)
    • writes permalink to text box on index.html
  • redrawControls()
    • Accesses where the svg paths for the control are using getElementByTagName() and sets them using jQuery's attr method.
    • This is called every time the graph sends a 'ready' event.

HTML+CSS

  • index.html, the file people load.
    • A small script detecting browser type adds the chrome class to #page-title. Without this page-title in chrome draws incorrectly, this is a chrome specific bug the tag draws correctly in all other browsers.
  • includes jquery.minm.1.7.2, for gviz api
  • main.js, draws graphs, handles data.
  • bootratp-button.js, enables buttons to behave like checkboxes. Toggling these buttons updates a global array of booleans held in main.js, and then redraws the graph depending on which are selected.
  • main.css, styling (some css3).
    • there are several blank css elements for the buttons in the beginning, keep these lines where they are as main.js accesses them by an index and gives them a background-color attribute.

Improvements and Bugs

  • Performance
    • More resolution in the graph will come at the expense of performance, right now points on the graph are averaged values from each hour. It would be better for graph readability if points were every, let's say, two minutes, however the graph would be unusable. Already the control chart is graphing every 30 lines of data, perhaps as the data range expands there could be less points on the chart itself and when the range is small it could show more resolution.

Harvest Scripts

There is a unique harvest script for every individual building, but the only differences between these scripts are the value of the ip number and the . The harvest scripts are called by cron every minute.

Procedure for the Script

  • 0. Sets a timestamp.
  • 1. Sets a variable $pReal to the return value of java --cp with the following arguments. This return value represents electrical energy usage.
 a. filepath to the production directory.
 b. ModbusReadDemand.
 c. The ip number of the EGX100 of the building in question.
 d. The amperage correction of the building in question.
  • 2. Connects to the energy database.
  • 3. Inserts into the electrical_energy table (located within the energy database) a row for the energy usage of the building in question at the time of the timestamp.

Building Monitoring Data

Buildings
Building Installed? EGX100 IP Directly off main switch? Amperage Correction Feed Size (Amps) Square Footage* Occupancy
Bundy Yes 159.28.165.100 No 31.25 800
Barrett Yes 159.28.165.101 No 15.625 1600
Wilson Yes 159.28.165.102 No 62.5 300/400
Warren Yes 159.28.165.103 No 62.5 300/400
Mills No 159.28.165.104 No unknown unknown
Olvey Andis Yes 159.28.165.105 No 31.25 800
Hoerner No No
Carpenter No No
Lilly Library No No 61,000
Runyon No Yes
Stanley No No
Dennis No No
Noyes No No
Tyler No No
LBC No Yes
Stout No no
Remainder No No Unknown (Total Feed Size - Measured Feed Sizes)
  •  : By Square Footage we mean the summed square footage of all the floors

Mills energy usage can be calculated as: Total_of_the_U - (Warren + Wilson)

Correct EGX100 Pin States

Pin Number 1 2 3 4 5 6
On | Off
  • NOTE: THE CORRECT PIN STATES ARE NOT YET KNOWN BY THE AUTHOR

Building Monitoring Plans

Because the energy wars focuses on student electricity usage, Residence Halls will be at the top of the priority list. 
To find out which buildings are not yet monitored, refer to the data table above.

Requisite Information for Equipment Installation

  • Wire size (diameter)
  • Amperage

Complications

  • The piecemeal history of electrical work in Earlham Hall makes separating cafeteria energy from Res Hall energy difficult.

Documentation