The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data
Abstract
Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.
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Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
VII. Note on regression and inheritance in the case of two parents
Pearson, K. (1895) · Proceedings of the Royal Society of London · doi:10.1098/rspl.1895.0041 · Cited by 3,424
Solar forecasting methods for renewable energy integration
Inman, R. H., Pedro, H. T. C., & Coimbra, C. F. M. (2013) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2013.06.002 · Cited by 1,062
Torres, O., Bhartia, P. K., Herman, J. R., Ahmad, Z., & Gleason, J. (1998) · Journal of Geophysical Research: Atmospheres · doi:10.1029/98jd00900 · Cited by 1,003
Artificial intelligence techniques for photovoltaic applications: A review
Mellit, A., & Kalogirou, S. A. (2008) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2008.01.001 · Cited by 1,001
Review of solar irradiance forecasting methods and a proposition for small-scale insular grids
Diagne, M., David, M., Lauret, P., Boland, J. W., & Schmutz, N. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.06.042 · Cited by 808
Shi, J., Lee, W., Liu, Y., Yang, Y., & Wang, P. (2012) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2012.2190816 · Cited by 795
Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems
Lorenz, E., Hurka, J., Heinemann, D., & Beyer, H. G. (2009) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2009.2020300 · Cited by 721
(2008) · Intelligent Infrastructure · doi:10.1201/9781482281767-9 · Cited by 665
Chen, C., Duan, S., Cai, T., & Liu, B. (2011) · Solar Energy · doi:10.1016/j.solener.2011.08.027 · Cited by 625
Cited by
Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109792 · Cited by 1,259
Forecasting of photovoltaic power generation and model optimization: a review
Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.017 · Cited by 1,211
Photovoltaic and solar power forecasting for smart grid energy management
Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. (2015) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2015.00046 · Cited by 654
Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope
Singh, A., Sharma, A., Ahmed, A., Sundramoorthy, A. K., Furukawa, H., Arya, S., & Khosla, A. (2021) · Biosensors · doi:10.3390/bios11090336 · Cited by 579
On recent advances in PV output power forecast
Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544
Metadata
- Authors
- Liu, J., Fang, W., Zhang, X., & Yang, C.
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2015-04
- Volume
- 6
- Issue
- 2
- Pages
- 434-442
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W2088786192
- Cited by
- 349
- References
- 26
- FWCI
- 28.47
- Type
- journal article
- Language
- en
- Open access
- closed
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