EE Seminar: Short-Term Spatiotemporal Photo-Voltaic power generation forecasting based on Interpolated Video Modeling
Electrical Engineering Systems Seminar
Speaker: Yanay Danan
M.Sc. student under the supervision of Dr. Jonatan Ostrometzky
Sunday, 23rd March 2025, at 15:30
Room 011, Kitot Building, Faculty of Engineering
Short-Term Spatiotemporal Photo-Voltaic power generation forecasting based on Interpolated Video Modeling
Abstract
Accurate short-term solar-based power forecasting is becoming a crucial element for efficient real-time power grid management, particularly in smart-grid systems, in which advanced technologies such as dynamic storage solutions are being implemented (e.g., for cost planning and vehicle to grid (V2G) applications). Cloud cover significantly impact photovoltaic (PV) power generation, often causing unexpected fluctuations. Since cloud dynamics are based on physical properties, their movement, which affect the PV power generation directly, can be effectively captured - and their movement predicted. Inspired by opportunistic sensing techniques used in weather monitoring via wireless communication channels, in this study I propose a novel data-driven approach that leverages the available PV power measurements to directly predict future disturbances in the expected power generation. Our approach first constructs a disturbance field from existing PV power snapshots and generates a video-like input using those snapshots, which is then processed by spatio-temporal video forecasting methods. Specifically, we utilize PredRNN++ as a recurrent-based model and SimVPv2 as a recurrent-free model to estimate the future evolution of these disturbances. Different from past works, here, we estimate the full field of the cloud-based power disturbances in a selected area of interest, rather than for pre-determined specific location. We achieve an nRMSE of 7.2% for forcasting horizons of 30 minutes, 9.1% for 60 minutes, and 11.6% for 2 hours, These results are close to the state-of-the-art prediction methodologies - but with the clear advantage of having the ability to forecast solar-generated power in locations from which no data is being collected, as well as to seamlessly adapt to any power-grid changes (with respect to the installment or removal of PV power generation elements) without requiring model retraining nor additional or new sensor data collection.
השתתפות בסמינר תיתן קרדיט שמיעה לתלמידי תואר שני ושלישי = עפ"י רישום שם מלא + מספר ת.ז. בדף הנוכחות שיועבר באולם במהלך הסמינר
