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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id-itb.:366592019-03-14T10:13:39ZSHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE Ahmad Hanafi, Rois Indonesia Theses Solar power forecasting, artificial neural networks, backpropagation, extreme learning machine. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/36659 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 text |
| institution |
Institut Teknologi Bandung |
| building |
Institut Teknologi Bandung Library |
| continent |
Asia |
| country |
Indonesia Indonesia |
| content_provider |
Institut Teknologi Bandung |
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| language |
Indonesia |
| description |
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 |
| format |
Theses |
| author |
Ahmad Hanafi, Rois |
| spellingShingle |
Ahmad Hanafi, Rois SHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE |
| author_facet |
Ahmad Hanafi, Rois |
| author_sort |
Ahmad Hanafi, Rois |
| title |
SHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE |
| title_short |
SHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE |
| title_full |
SHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE |
| title_fullStr |
SHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE |
| title_full_unstemmed |
SHORT-TERM SOLAR POWER FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND EXTREME LEARNING MACHINE |
| title_sort |
short-term solar power forecasting using artificial neural networks and extreme learning machine |
| url |
https://digilib.itb.ac.id/gdl/view/36659 |
| _version_ |
1821997186324365312 |