| Title |
Short-Term Load Forecasting Algorithm on Weekdays considering the Amount of Behind-the-Meter Generation |
| Authors |
Dong-Jin Bae ; Bo-Sung Kwon ; Chan-Ho Moon ; Su-Hwa Woo ; Kyung-Bin Song |
| DOI |
http://doi.org/10.5207/JIEIE.2020.34.11.037 |
| Keywords |
Short-term Load Forecasting; Behind-the-Meter Generation; Reconstituted Load Method |
| Abstract |
The short-term load forecasting(STLF) is necessary for stable and efficient power system operation. With the spread of renewable energy, solar photovoltaic(PV) capacity is increasing. Most solar PV generator in South Korea is classified Behind-the-Meter(BTM) generator, such as small capacity of solar PV and distributed energy resources(DER). BTM generation is one of the main factors that change the load, and it causes the uncertainty of load forecasting. As a high penetration of BTM solar PV generator, load forecasting error is increased. The load forecasting algorithm using reconstituted load method is proposed to accurate load forecasting considering the BTM generation. The load forecast is performed for weekday excluding special days for the case studies in 2019. The proposed algorithm is improved a lot of the accuracy of STLF compared to the former STLF algorithms that do not considering BTM generation. |