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Determination Method of Insolation Prediction With Fuzzy and Applying Neural Network for Long-Term Ahead PV Power Output Correction
Abstract
In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and the output of a photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV systems as accurately as possible, an insolation estimation method is required. This paper proposes the power output forecasting of a PV system based on insolation forecasting at 24 hours ahead by using weather reported data, fuzzy theory, and neural network (NN). If the suitable training data is not selected, the training process of NN tends to be unstable. The proposed technique for application of NN is trained by power output data based on fuzzy theory and weather reported data. Since the fuzzy model determines the insolation forecast data, NN will train the power output smoothly. The validity of the proposed method is confirmed by comparing the forecasting abilities on the computer simulations.
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Citations
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
Solar photovoltaic generation forecasting methods: A review
Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.11.019 · Cited by 990
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
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
- Yona, A., Senjyu, T., Funabashi, T., & Kim, C.-H.
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2013-04
- Volume
- 4
- Issue
- 2
- Pages
- 527-533
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W2073603371
- Cited by
- 191
- References
- 0
- FWCI
- 15.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Determination Method of Insolation Prediction With Fuzzy and Applying Neural Network for Long-Term Ahead PV Power Output Correction. https://app.cochat.ai/papers/10.1109/tste.2013.2246591
- The paper
- Yona, A., Senjyu, T., Funabashi, T., & Kim, C.-H. (2013). Determination Method of Insolation Prediction With Fuzzy and Applying Neural Network for Long-Term Ahead PV Power Output Correction. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/tste.2013.2246591
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
