A Hybrid Data-Driven Online Solar Energy Disaggregation System from the
Grid Supply Point
Abstract
The integration of small-scale PV systems (such as roof-top PVs)
decreases the visibility of the power system since the real demand load
is masked. Most of the rooftop systems are behind-the-meter and cannot
be measured by the household smart meter. To overcome the challenges
mentioned above, this paper proposes an online solar energy
disaggregation system to decouple the solar energy generated by the
roof-top PV systems and ground truth demand load from the net
measurements. A 1D CNN bidirectional long short-term memory (CNN-BiLSTM)
deep learning method is used as the core algorithm of the proposed
system. The system takes a wide range of online information (AMI data,
meteorological data, satellite-driven irradiance, and temporal
information) as inputs to evaluate the PV generation, and the system
also enables online and offline modes. The effectiveness of the proposed
algorithm is evaluated by comparing it to baselines. The results show
that the proposed method reaches good performance under different
penetration rates and different feeder levels. Finally, a transfer
learning process is introduced to verify the proposed system has good
robustness and can be applied to anywhere else easily.