| Title |
DLinear-BiLSTM hybrid gross load estimation model considering the absence of BTM PV generation data |
| Authors |
현동호(Dong-Ho Hyun) ; 반재필(Jaepil Ban) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.2.266 |
| Keywords |
Gross load estimation; Net load; Behind-the-meter; Photovoltaic generation; Intelligent control |
| Abstract |
The expanding adoption of distributed photovoltaic (PV) generators introduces uncertainties into power system operation. In particular, as distribution system operators often observe only substation net load, it can be a challenging issue to obtain the gross load by separating the unobserved behind-the-meter (BTM) PV from the net load. This paper proposes a hybrid DLinear?BiLSTM, model to estimate the gross load when BTM PV measurements are unavailable. The proposed model uses a DLinear-based decomposition to split the time series input data into trend and seasonal components. Then, a bidirectional LSTM jointly learns nonlinear correlations and long- and short-term dependencies. In particular, because it uses only accessible exogenous variables such as public meteorological data (solar irradiance, temperature, precipitation/snowfall), calendar factors, and a region identifier, the method is easy to implement and scale without detailed facility-level data. The proposed model is validated by the experiment using one-year dataset obtained from a substation. |