VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration

Technical Report · Thu Jan 08 00:00:00 EST 2026

DOI:https://doi.org/10.2172/3012343· OSTI ID:3012343

Kundu, Soumya; Campbell, Allison M.; Bhattacharjee, Kaustav; Vasios, Orestis; Reiman, Andrew P.; Chakraborty, Indrasis

This is the final technical report for the SETO-funded VRN3P project (PNNL# 76914). The goal of this project, led by Pacific Northwest National Laboratory (PNNL), in collaboration with Lawrence Livermore National Laboratory (LLNL) and Portland General Electric (PGE), was to develop and validate a deep variational recurrent neural network-based net-load prediction (VRN3P) framework for probabilistic time-series forecasting of day-ahead net-load under high solar penetration scenarios. The project team reports successful design of a novel probabilistic net-load forecasting architecture, comprising of a variational autoencoder and a recurrent neural network, which demonstrates 30% improvement in forecast performance, 60% improvement in training time, and consumes 44% less memory, when compared with conventional baseline models. The team tested the VRN3P model performance on GridLAB-D test-cases representing varying BTM solar penetration levels of 20%, 30%, and 50%, with integrated time-series net-load profiles provided by the utility partner (PGE). The VRN3P model demonstrate <2% hourly MAPE (averaged over the year) for day- ahead net-load forecast on the test scenario with 20% BTM solar. Transfer learning extension of the VRN3P model has demonstrated 8.33× speed-up in training, while still achieving acceptable forecast performance of 2.24% hourly MAPE on the 30% BTM solar penetration test-scenario. A preliminary version of the VRN3P GridAPPS-D™has been developed, along with a web-based interactive user-interface (named ‘Forte’) which has made available on GitHub for public use.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 3012343
• Report Number(s): PNNL-38890
• Country of Publication: United States
• Language: English