Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction
Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural mod...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/8699/ http://eprints.uthm.edu.my/8699/1/J14369_830d1175165a60a814f4f04bf869a007.pdf |
| Summary: | Accurate photovoltaic (PV) power prediction has
been a subject of ongoing study in order to address grid stability
concerns caused by PV output unpredictability and intermittency.
This paper proposes an ultra-short-term hybrid photovoltaic
power forecasting method based on a dendritic neural model
(DNM) in this paper. This model is trained using improved
biogeography-based optimization (IBBO), a technique that incorporates a domestication operation to increase the performance
of classical biogeography-based optimization (BBO). To be more
precise, a similar day selection (SDS) technique is presented
for selecting the training set, and wavelet packet transform
(WPT) is used to divide the input data into many components.
IBBO is then used to train DNM weights and thresholds for
each component prediction. Finally, each component’s prediction
results are stacked and reassembled. The suggested hybrid
model is used to forecast PV power under various weather
conditions using data from the Desert Knowledge Australia Solar
Centre (DKASC) in Alice Springs. Simulation results indicate
the proposed hybrid SDS and WPT-IBBO-DNM model has the
lowest error of any of the benchmark models and hence has the
potential to considerably enhance the accuracy of solar power
forecasting (PVPF). |
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