Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation

Wind power is one of the most vital renewable energy resources in the world. Wind energy production is directly correlated with the quality and quantity of wind speed data. Interpolation techniques can be employed to fill in the gaps in the current wind speed observation data series. However, existing methods for obtaining blank data do not pre-regulate the regional spatial wind speed sequence and instead rely on direct interpolation, which leads to low accuracy in the interpolation. This is not a problem with the model itself, but rather with the wind speed flowing in space and exhibiting sequence misalignment on the timeline. To address this issue, this study proposes a Wind Speed Dynamic Time Warping (WSDTW) algorithm based on Dynamic Time Warping (DTW) to match similar wind speed reduction sequences in terms of time error. We used the shape context descriptor to encode wind speed and introduced wind rose descriptors to represent wind direction initially. The matching cost of DTW was then optimized. Finally, five common interpolation methods were selected to evaluate the method. The research results indicate that interpolation after WSDTW matching and warping can significantly improve the accuracy of wind speed interpolation and reduce the spatial dependence of wind speed. This method demonstrates good stability and generalization, performing exceptionally well in situations where there are gaps in regional wind speed data or missing data.