Open Energy View

The goal of this project is to analyze and present resource consumption data to users empowering them to conserve and save money.

Open Beta!

https://www.openenergyview.com

If you are a PG&E customer you can link your account now! If you are not a PG&E customer you can try the demo and talk to J.P. about integrating your utility.

User Interface

Interface

Design

Development

Environment Setup (Ubuntu 20.04)

Process notes here: https://github.com/JPHutchins/open-energy-view/issues/31

The following notes are for setting up the environment with a Windows 10 host and Ubuntu 20.04 on WSL2. Please submit a PR if you find necessary adaptations on your environment.

Personally I use VSCode from the Windows host utilizing the "Remote - SSH" and "Remote - WSL" extensions.

Clone this repository

git clone git@github.com:JPHutchins/open-energy-view.git
cd open-energy-view

Install backend dependencies

Install python requirements

Note: check your python3 version

sudo apt install python3.8-venv build-essential python3-dev

Create the virtual environment and install packages

python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

Install and configure rabbitmq

Install erlang:

sudo apt update
sudo apt install software-properties-common apt-transport-https
wget -O- https://packages.erlang-solutions.com/ubuntu/erlang_solutions.asc | sudo apt-key add -
echo "deb https://packages.erlang-solutions.com/ubuntu focal contrib" | sudo tee /etc/apt/sources.list.d/rabbitmq.list
sudo apt update
sudo apt install erlang

Install rabbitmq


curl -s https://packagecloud.io/install/repositories/rabbitmq/rabbitmq-server/script.deb.sh | sudo bash
sudo apt install rabbitmq-server

Start rabbitmq-server
```
sudo service rabbitmq-server start
```
Verify that rabbitmq-server is running
```
sudo service rabbitmq-server status
```

Configure rabbitmq

sudo rabbitmqctl add_user jp admin
sudo rabbitmqctl set_user_tags jp administrator
sudo rabbitmqctl add_vhost myvhost
sudo rabbitmqctl set_permissions -p myvhost jp ".*" ".*" ".*"

Install frontend dependencies and build

Install nvm (if you don't have it)

notes: https://github.com/nvm-sh/nvm

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.1/install.sh | bash

Install npm
```
nvm install 10.19.0
```

Install frontend packages

cd open_energy_view/frontend
nvm use 10
npm install

Build frontend

Assumes you are at path: */open-energy-view/open_energy_view/frontend
```
nvm use 10
npm run build
```

Run the development server and workers

Start the server and workers
- Open four terminals (example from VSCode)
- First terminal: ./run-wsgi-dev
- Second terminal: ./run-io-worker
- Third terminal: ./run-cpu-worker
Open the development site in a browser
- Fourth terminal: ip a
- Note the IP address of your WSL2 instance, in this case 172.31.30.203
- On your host OS, open a Chrome or Firefox web browser and navigate to http://<YOUR_WSL2_IP>:5000

Example account setup

For first time setup you must register a user to your local database. Use something easily memorable and keep in mind that you can register as many users as you need while testing.

Click "Register now!"
- For email use: dev@dev.com
- For password use: admin
You are prompted to add energy sources. This is local development so there would be no way to add a real PGE account here. In the dropdown select "Fake Utility" and click "Authorize".
You are prompted to name the fake energy source. Enter dev, for example, then click "Add Source".

After simulating API calls and parsing the retrieved ESPI data (J.P.'s old data) you will be greeted with an OEV instance that will respond to changes in your local Python/Flask/Celery backend and React frontend.

Data Analysis

Averages

Data is presented always by Watt hour (Wh). This is so that the user can compare different time intervals to one another. It is not meaningful to compare 630,000 Watts consumed in the month of June to 30,000 Watts consumed last Tuesday. Rather, we would like to understand the intensity of usage (average) of different time intervals. Usefully, we can see that the average 1,250 Watt hours consumed last Tuesday is higher than the average 875 Watt hours consumed during June.

Partitions

A partition is a time interval that recurs each day. The default partitions are:

Night: 12AM -> 7AM
Day: 7AM -> 6PM
Evening: 6PM -> 12AM

These partitions allow the user to develop conclusions about what activities are using the most resources.

Passive Consumption

Passive consumption is the amount of a resource a building will use even when no person is actively utilizing the energy. This is calculated statistically using a rolling mean and rolling standard deviation. This passive consumption metric can account for an outsized amount of electricity utilized in a building since it is by definition always consuming energy.

Presentation of this very useful metric allows users to understand the impact of passive appliances on their resource consumption as well as empowers them to find and disable devices that they do not need 24/7.

Activies Pie Chart

The activities pie chart shows the user how much power each activity consumed over the current time window.

Trends

Various trends are calculated to give the user realtime feedback on their resource conservation efforts.

Seasonal Trend

This tend shows about how much power the user is using during this "time of the year" this year vs this "time of the year" last year. The time range is +/- 14 days to attempt to mitigate the impact of statistical outliers like unseasonably hot or cold weather.

Active Use Trend

This trend shows the active use trend up to this point. Examples of active use:

Appliances
TVs
Computers
Lighting
Background (Passive) Trend

This trend shows the passive used trend up to this point. Examples of passive use:
Network Equipment
Security Systems
HVAC
IoT devices

Resources

Green Button and ESPI