A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing...

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Bibliographic Details
Published in:Atmosphere 2023-10, Vol.14 (10), p.1516
Main Authors: Reja, R. K., Amin, Ruhul, Tasneem, Zinat, Abhi, Sarafat Hussain, Bhatti, Uzair Aslam, Sarker, Subrata Kumar, Ain, Qurat ul, Ghadi, Yazeed Yasin
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
artificial neural network
Artificial neural networks
Clean energy
Clean technology
co-efficient of performance
Electricity
Electricity distribution
Energy consumption
Energy resources
Energy shortages
Energy sources
Fossil fuels
Fuel combustion
Greenhouse gases
Machine learning
Methods
Multivariate analysis
Neural networks
Renewable energy
Renewable resources
short-term prediction
Speed
Supervisory control and data acquisition
Time series
time series prediction
Wind direction
Wind farms
Wind forecasting
Wind measurement
Wind power
Wind speed
Winds
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Summary:The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos14101516