A mutated hybrid Cuckoo Search Artificial neural network for Grid-Connected Photovoltaic system output prediction / Norfarizani Nordin

This thesis presents a hybrid technique for predicting the AC power output from a Grid-Connected Photovoltaic (GCPV) system. Initially, the prediction was conducted using six classical Multi-Layer Feedforward Neural Network (MLFNN) models. These models were developed based on different sets of input...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Nordin, Norfarizani
التنسيق: أطروحة
اللغة:English
منشور في: 2019
الوصول للمادة أونلاين:https://ir.uitm.edu.my/id/eprint/91415/1/91415.pdf
https://ir.uitm.edu.my/id/eprint/91415/
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الوصف
الملخص:This thesis presents a hybrid technique for predicting the AC power output from a Grid-Connected Photovoltaic (GCPV) system. Initially, the prediction was conducted using six classical Multi-Layer Feedforward Neural Network (MLFNN) models. These models were developed based on different sets of inputs. A key feature for developing these models is the inclusion of time-series inputs. The inclusion of time-series inputs to the network is important as the solar irradiance, ambient temperature and module temperature have different time-constant; i.e. they have different rate of change as the climate changes.