Pregled bibliografske jedinice broj: 1142976
A Satellite Image Data based Ultra-short-term Solar PV Power Forecasting Method Considering Cloud Information from Neighboring Plant
A Satellite Image Data based Ultra-short-term Solar PV Power Forecasting Method Considering Cloud Information from Neighboring Plant // Energy (Oxford), 238 (2022), C; 121946, 16 doi:10.1016/j.energy.2021.121946 (međunarodna recenzija, članak, znanstveni)
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Naslov
A Satellite Image Data based Ultra-short-term
Solar PV Power Forecasting Method Considering
Cloud Information from Neighboring Plant
Autori
Wang, Fei ; Lu, Xiaoxing ; Mei, Shengwei ; Su, Ying ; Zhen, Zhao ; Zou, Zubing ; Zhang, Xuemin ; Yin, Rui ; Duić, Neven ; Shafie-khah, Miadreza ; Catalão, João P.S.
Izvornik
Energy (Oxford) (0360-5442) 238
(2022), C;
121946, 16
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Ultra-short-term ; PV power forecasting ; Spatio-temporal ; Satellite image
Sažetak
Accurate ultra-short-term PV power forecasting is essential for the power system with a high proportion of renewable energy integration, which can provide power fluctuation information hours ahead and help to mitigate the interference of the random PV power output. Most of the PV power forecasting methods mainly focus on employing local ground-based observation data, ignoring the spatial and temporal distribution and correlation characteristics of solar energy and meteorological impact factors. Therefore, a novel ultra-short- term PV power forecasting method based on the satellite image data is proposed in this paper, which combines the spatio-temporal correlation between multiple plants with power and cloud information. The associated neighboring plant is first selected by spatial-temporal cross- correlation analysis. Then the global distribution information of the cloud is extracted from satellite images as additional inputs with other general meteorological and power inputs to train the forecasting model. The proposed method is compared with several benchmark methods without considering the information of neighboring plants. Results show that the proposed method outperforms the benchmark methods and achieves a higher accuracy at 4.73%, 10.54%, and 4.88%, 11.04% for two target PV plants on a four-month validation dataset, in terms of root mean squared error and mean absolute error value, respectively.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
