This is a forecasting and analytics engine. Given certain assumptions on the existence, and quality of data, it provides:
git clone https://github.com/dkarapetyan/larkinin a unix shell.
pip -e install $PROJECT_ROOT. After installation, do not move the
$PROJECT_ROOTdirectory, as this will break the installation.
Once installation is successful, execute
run_analytics in a bash shell
(it is automatically added to your
PATH environment variable
by the installation process). This is the entry point for the analytics
The user will need to add the following variables to the shell environment from which the suite is run: WUND_URL, DB_HOST, DB_PORT, DB_SOURCE, DB_USERNAME, and DB_PASSWORD.
Please make sure to copy over the test weather database (history and forecasts tables) currently being used by analytics. We have built an archive of forecast data that is required for the feature of running previous predictions to work properly.
For options and features, please execute
in your shell.
If a bms prediction time does not exist for the building, or can’t be computed from the available data, a sentinel value of “2200-01-01 00:00:00+0000”, representing ‘infinity’, will be outputted by the model.
After installation, please setup a scheduler to
every 15 minutes, in order to continue adding weather data to the historical
and forecast tables. Failing to do so may result in the failure of
the ‘previous prediction’ feature for certain dates.
Given the current amount of data, the model takes a maximum of about 15-20 minutes for bms predictions, assuming the existence of significant points for that building in the configuration file, and may take longer for those without. Please use this information in your cloud scheduling planning.