Solar insolation forecast using artificial neural network for Malaysian weather
Solar insolation forecast is essential for photovoltaic (PV) generation plant in enhancing the usage of solar energy for electrical production scheme. Likewise, it improves thi PV power generation efficiency by regulating the control algorithm and charge controller corresponding to the prediction pr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2012
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6074/ http://eprints.uthm.edu.my/6074/1/SOLAR_INSOLATION_FORECAST_USING_ARTIFICIAL.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Solar insolation forecast is essential for
photovoltaic (PV) generation plant in enhancing the usage
of solar energy for electrical production scheme. Likewise, it
improves thi PV power generation efficiency by regulating
the control algorithm and charge controller corresponding
to the prediction probability. An acquired data in solar
insolation is required for solar energy harvesting. This
paper presents the 12 hourly solar insolation forecast using
Artificial Neural Network (ANN). A Multi-level perceptron
(MLP) with back propagation technique model is proposed
to predict the next day 12 hours solar insolation. The
performance of MLP model is investigated with 60 days of
solar insolation data from July 9th to September 9th 2011.
The investigation is conducted under two different tropical
weather conditions, sunny and rainy conditions. Hence, the
best performance MLP forecaster model with a minimal
error is selected through a trial and error method under
various weather conditions. In this paper, the performance
of the forecaster is shown. The results allow inferring the
adequate performance and pertinence of this methodology
to predict complex phenomena, such as solar insolation. |
|---|