Missing and low-fidelity data presents a barrier to renewable growth when utilities can't forecast or analyze solar production. In a novel project with Kit Carson Electric Cooperative, Camus Energy, and PNNL apply proven machine learning techniques to fill gaps in missing solar data, enabling KCEC to reliably operate their grid with 100% daytime solar supply. Participants will understand this novel approach and what other utilities can do to leverage similar tools for improved grid visibility and faster renewable adoption.
