Portfolio

Synopsis

This is a forecasting and analytics engine. Given certain assumptions on the existence, and quality of data, it provides:

  • Building BMS beginning of day rampup recommendation, with confidence intervals (given by ‘left_bound’ and ‘right_bound’).
  • Building electric demand, steam demand, water consumption, and occupancy predictions, with confidence intervals (given by ‘left_bound’ and ‘right_bound’).

Installation

  • Install the Python 2.7 version of Anaconda 4.0.0 (64-bit).
  • Download the desired release of the analytics suite to your local hard drive. If you have a local copy of git, this can be done by running git clone https://github.com/dkarapetyan/larkin in a unix shell.
  • From a unix shell, run pip -e install $PROJECT_ROOT. After installation, do not move the $PROJECT_ROOT directory, as this will break the installation.

Execution

  • 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 suite.

  • 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.

Options and Features

  • For options and features, please execute run_analytics -h 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.

Scheduling on the Cloud

  • After installation, please setup a scheduler to execute run_analytics --weather_update 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.

Contributors

License

  • Proprietary