Deep-Learning-Based, Multi-Timescale Load Forecasting in Buildings:
Opportunities and Challenges from Research to De-ployment
Abstract
Electricity load forecasting for buildings and campuses is becoming
increasingly important as the penetration of distributed energy
resources (DERs) grows. Efficient operation and dispatch of DERs require
reasonably accurate predictions of future energy consumption in order to
conduct near-real-time optimized dispatch of on-site generation and
storage assets. Electric utilities have traditionally performed load
forecasting for load pockets spanning large geographic areas, and
therefore forecasting has not been a common practice by buildings and
campus operators. Given the growing trends of research and prototyping
in the grid-interactive efficient buildings domain, characteristics
beyond simple algorithm forecast accuracy are important in determining
the algorithm’s true utility for smart buildings. Other characteristics
include the overall design of the deployed architecture and the
operational efficiency of the forecasting system. In this work, we
present a deep-learning-based load forecasting system that predicts the
building load at 1-hour intervals for 18 hours in the future. We also
discuss challenges associated with the real-time deployment of such
systems as well as the research opportunities presented by a fully
functional forecasting system that has been developed within the
National Renewable Energy Laboratory’s Intelligent Campus program.