skip to main content

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

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.
 [1] ;  [1] ;  [1] ;  [2] ;  [1] ;  [1]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
Publication Date:
OSTI Identifier:
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: 3rd International High Performance Buildings Conference at Purdue, West Lafayette, IN, USA, 20140714, 20140717
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Building Technologies Research and Integration Center (BTRIC)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States