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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書目詳細資料
主要作者: Nordin, Norfarizani
格式: Thesis
語言:English
出版: 2019
在線閱讀:https://ir.uitm.edu.my/id/eprint/91415/1/91415.pdf
https://ir.uitm.edu.my/id/eprint/91415/
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總結: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.