Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-base...

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Bibliographic Details
Published in:Energies (Basel) Vol. 14; no. 2; p. 436
Main Authors: Ahn, Hyung Keun, Park, Neungsoo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2021
Subjects:
Accuracy
Algorithms
Alternative energy sources
Correlation analysis
Electricity distribution
Experiments
Humidity
Internet of Things
Internet of Things (IoT)
Neural networks
Onsite
photovoltaic power forecasting algorithm
recurrent neural networks (RNN)
Renewable resources
Sensors
Solar energy
Wavelet transforms
Weather
Weather forecasting
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R2-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en14020436