Application Of An Operational Meso-Scale Modelling System For Industiral Plant Energy Operations

In our continuing work on the implementation and applications of a mesoscale modelling system dubbed "Deep Thunder", we examine its application for industrial plant energy management operations. The Deep Thunder system has provided 24-hour forecasts for several metropolitan regions in the United States for a number of years. It has recently been extended to enable up to 84-hour forecasts at a similar scale. Model forecasts, are typically updated twice daily with triply, nested grids down to the meso-gamma-scale. Explicit, bulk cloud microphysics are included in the model predictions to enable forecasts of potentially severe weather. All of the processing, modelling and visualization are completed in approximately one hour per 24-hours of forecast time on relatively modest hardware to enable sufficiently timely dissemination of forecast products for potential weather-sensitive applications.

Some of the recent extensions to Deep Thunder have included support for weather-sensitive operations at several major IBM facilities in the northeastern United States. These facilities range from large office complexes to research facilities and semi-conductor manufacturing plants. To enable environmental analytics relevant to such sites, we are investigating the use of high-resolution numerical weather prediction models in optimizing energy management strategies for smart building applications. The NWP model provides highly granular (temporally and spatially) predictive information (e.g., temperature) that is directly relevant for HVAC operations and electrical load shedding algorithms. As part of the work, we are attempting to quantify the potential for improving energy and cost savings for industrial building and site operations. We will discuss our overall approach to these issues, the specific site issues, and some of the results to date.