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 mult iple 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.