The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 29-494
Full Name: Shahril Irwan Sulaiman
Position: Lecturer
Age: ON
Sex: Male
Address: Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
Country: MALAYSIA
Tel: +6 03 55436031
Tel prefix:
Fax:
E-mail address: shahril_irwan2004@yahoo.com
Other E-mails: shahril@salam.uitm.edu.my
Title of the Paper: Prediction of Grid-Photovoltaic System Output Using Three-variate ANN Models
Authors as they appear in the Paper: Shahril Irwan Sulaiman, Ismail Musirin, Titik Khawa Abdul Rahman
Email addresses of all the authors: shahril_irwan2004@yahoo.com, i_musirin@yahoo.co.uk, takitik@streamyx.com
Number of paper pages: 10
Abstract: This paper presents the prediction of total AC power output from a grid-photovoltaic system using three-variate artificial neural network (ANN) models. In this study, two-hidden layer feedforward ANN models for the prediction of total AC power output from a grid-connected photovoltaic system have been considered. Three different models were configured based on different sets of ANN inputs. In addition, each model utilizes three types of inputs for the prediction. The first model utilizes solar radiation, wind speed and ambient temperature as its inputs while the second model uses solar radiation, wind speed and module temperature as its inputs. The third model uses solar radiation, ambient temperature and module temperature as its inputs. Nevertheless, all the three models employ similar type of output which is the total AC power produced from the grid-connected system. Data filtering process has been introduced to select the quality data patterns for training proc!
ess, making only the informative features are available. Thus, the regression analysis and root mean square error (RMSE) performance of each model could be enhanced. After the training process is completed, the testing process is performed to decide whether the training process should be repeated or stopped. Besides selecting the best prediction model, this study also exhibits some of the experimental results which illustrate the effectiveness of the data filtering in predicting the total AC power output from a grid-connected system. Each ANN model was tested with Levenberg-Marquardt training algorithm and scaled conjugate gradient training algorithm to select the best training algorithm for each model. Fully trained ANN model should later be able to predict the AC power output from a set of un-seen data patterns.
Keywords: Artificial neural network (ANN), Photovoltaic (PV), Regression coefficient (R), Root mean square (RMSE), Prediction, Solar radiation (SR), Ambient temperature (AT), Wind speed (WS), AC power
EXTENSION of the file: .doc
Special (Invited) Session: Prediction of Total AC Power Output from a Grid-Photovoltaic System Using Multi-model ANN
Organizer of the Session: 590-237
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