TY - GEN
T1 - Day-Ahead PV Power Forecasting for Control Applications
AU - Ürkmez, Mirhan
AU - Kallesøe, Carsten
AU - Bendtsen, Jan Dimon
AU - Leth, John-Josef
PY - 2022/12
Y1 - 2022/12
N2 - The percentage of solar energy among all installed energy sources is increasing each year. Photo-voltaic (PV) power forecasting is key to the application of control methods in systems with PV panels. In this paper, we present a method for day-ahead PV power forecasting at each time step that is easy to train and can be applied to different power data types (e.g data from hot and cold climates, with various sampling times). Predictions made before and after sunrise are handled separately. Exponentially Weighted Moving Average (EWMA) is applied on the normalized daily power data to estimate the shape of the next-day power curve for the predictions before sunrise. Then, the multiplier value which would expectedly produce the best forecast when multiplied with the estimated shape is predicted using a time-series approach. After sunrise, the observed power data is leveraged to improve the previous forecasts. The proposed method is shown to perform well on multiple data sets with varying characteristics. Also, the method is compared with some benchmarks algorithms, and the results are presented.
AB - The percentage of solar energy among all installed energy sources is increasing each year. Photo-voltaic (PV) power forecasting is key to the application of control methods in systems with PV panels. In this paper, we present a method for day-ahead PV power forecasting at each time step that is easy to train and can be applied to different power data types (e.g data from hot and cold climates, with various sampling times). Predictions made before and after sunrise are handled separately. Exponentially Weighted Moving Average (EWMA) is applied on the normalized daily power data to estimate the shape of the next-day power curve for the predictions before sunrise. Then, the multiplier value which would expectedly produce the best forecast when multiplied with the estimated shape is predicted using a time-series approach. After sunrise, the observed power data is leveraged to improve the previous forecasts. The proposed method is shown to perform well on multiple data sets with varying characteristics. Also, the method is compared with some benchmarks algorithms, and the results are presented.
KW - Control applications
KW - PV power forecasting
KW - Renewable energy
UR - http://www.scopus.com/inward/record.url?scp=85143906476&partnerID=8YFLogxK
U2 - 10.1109/IECON49645.2022.9968709
DO - 10.1109/IECON49645.2022.9968709
M3 - Article in proceeding
T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
BT - 2022 IECON – 48th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE
T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Y2 - 17 October 2022 through 20 October 2022
ER -