Integration of Photovoltaics into Building Energy Usage through Advanced Control of Rooftop Unit

Conference · Wed Jan 01 00:00:00 EST 2014

OSTI ID:1148876

Starke, Michael R ^[1]; Nutaro, James J ^[1]; Irminger, Philip ^[1]; Ollis, Benjamin ^[2]; Kuruganti, Phani Teja ^[1]; Fugate, David L ^[1]

---

1. ORNL
2. University of Tennessee, Knoxville (UTK)

This paper presents a computational approach to forecast photovoltaic (PV) power in kW based on a neural network linkage of publicly available cloud cover data and on-site solar irradiance sensor data. We also describe a control approach to utilize rooftop air conditioning units (RTUs) to support renewable integration. The PV forecasting method is validated using data from a rooftop PV panel installed on the Distributed Energy, Communications, and Controls (DECC) laboratory at Oak Ridge National Laboratory. The validation occurs in multiple phases to ensure that each component of the approach is the best representation of the actual expected output. The control of the RTU is based on model predictive methods.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center (BTRIC)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1148876
• Resource Relation: Conference: 3rd International High Performance Buildings Conference at Purdue, West Lafayette, IN, USA, 20140714, 20140717
• Country of Publication: United States
• Language: English