SHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE
Indonesia is a tropical country with massive potential of solar power. The grid operator has to prepare the strategies to mitigate its intermittency behaviour. The power grid must be more flexible to receive the fluctuated power from PV. There are some options to enhance system flexibility in dea...
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| Main Author: | |
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/36659 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Indonesia is a tropical country with massive potential of solar power. The grid
operator has to prepare the strategies to mitigate its intermittency behaviour. The
power grid must be more flexible to receive the fluctuated power from PV. There
are some options to enhance system flexibility in dealing with renewable energy
integration. One of the economical ways is to conduct a solar power forecasting.
On the other hand, the use of machine learning is getting more popular in recent
days. It has many applications including in prediction. In this paper, the solar
power forecasting was conducted using artificial neural networks to predict the
next hour of PV power output. The networks were trained using backpropagation
(BPNN) and extreme learning machine (ELM). The historical PV power output,
weather condition and time were used as the inputs. The extreme learning
machine was used due to its advantages in accuracy and computational time over
backpropagation neural networks |
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