Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Citations Over TimeTop 10% of 2021 papers

Abstract

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

Related Papers

• → Analysis of the Wind Turbine Selection for the Given Wind Conditions(2021)27 cited
• → Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm(2017)17 cited
• → Realization of Small Wind Turbines for Low-Speed Wind Regions(2023)1 cited
• → Research on Fault early Warning Technology of Wind Turbine Main Bearing by Stacked Auto Encoder(2021)
• → PERFORMANCE EVALUATION OF SOME SELECTED WIND TURBINES IN HEIPANG WIND SPEED REGIME(2022)