Lead Performer: National Renewable Energy Laboratory – Golden, CO; partner: Earth Networks – Germantown, MD
December 4, 2018
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Lead Performer: National Renewable Energy Laboratory – Golden, CO
Partner: Earth Networks – Germantown, MD
DOE Total Funding: $749,553
FY19 DOE Funding: $250,000
Project Term: October 1, 2018 – September 30, 2021
Funding Type: Lab Award
Project Objective
Site-specific weather information is crucial for operating grid-interactive efficient buildings (GEB), but it is generally unavailable or expensive to obtain. This project aims to develop a foundational platform to provide site-specific weather forecasts by using low-cost, total-sky imagers in conjunction with readily available weather station data. This project will identify energy savings potential in GEBs by integrating:
- Site-specific weather forecasts, provided by advanced machine learning methods like deep neural networks, to capture the spatiotemporal correlations between the local weather conditions and nearby weather station data.
- Building energy forecasts from model predictive control (MPC) that incorporates the site-specific weather forecast and co-simulates with building models in EnergyPlus.
- Data analytics for evaluating the accuracy of weather and building energy forecasts, and for understanding how the site-specific weather forecasts, building types, and climates affect the energy savings in GEB.
Project Impact
This project seeks to fundamentally advance the building science of integrating site-specific weather forecasting with advanced building controls to ultimately reduce energy use. This project will provide generalizable guidelines on the appropriate level of site-specific weather forecasting and its impact on building energy savings improvement in different types of buildings and climates.
Contacts
DOE Technology Manager: Erika Gupta
Lead Performer: Rui Yang, National Renewable Energy Laboratory