Abstract
Photovoltaic (PV) module temperature is an important parameter in PV system operation as the system output power decreases as the module temperature increases. Therefore, the modeling of operating PV module temperature is crucial to understand the climatic factors which contribute to the variation of the PV module temperature. This paper presents the modeling of operating PV module temperature from a Grid-Connected Photovoltaic (GCPV) system located at Green Energy Research Centre (GERC), Universiti Teknologi MARA, Malaysia. An Artificial Neural Network (ANN) was developed to model the operating PV module temperature with solar irradiance and ambient temperature set as the ANN inputs. In addition, Cuckoo Search (CS) was introduced to search for the optimal number of neurons of ANN hidden layer, learning rate and momentum rate such that the Mean Absolute Percentage Error (MAPE) of the modeling process could be minimized. The results showed that CS had outperformed an Artificial Bee Colony (ABC) algorithm for the ANN training optimization by producing lower MAPE.
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Sulaiman, S.I., Zainol, N.Z., Othman, Z., Zainuddin, H. (2014). Modeling of Operating Photovoltaic Module Temperature Using Hybrid Cuckoo and Artificial Neural Network. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_3
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DOI: https://doi.org/10.1007/978-3-319-13332-4_3
Publisher Name: Springer, Cham
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