Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting

Yingya Zhou, Linwei Ma (), Weidou Ni and Colin Yu
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Yingya Zhou: State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
Linwei Ma: State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
Weidou Ni: State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
Colin Yu: Chenqiao Smart Technology, Inc., Shanghai 201306, China

Energies, 2023, vol. 16, issue 5, 1-18

Abstract: Wind power forecasting involves data preprocessing and modeling. In pursuit of better forecasting performance, most previous studies focused on creating various wind power forecasting models, but few studies have been published with an emphasis on new types of data preprocessing methods. Effective data preprocessing techniques and the fusion with the physical nature of the wind have been called upon as potential future research directions in recent reviews in this area. Data enrichment as a method of data preprocessing has been widely applied to forecasting problems in the consumer data universe but has not seen application in the wind power forecasting area. This study proposes data enrichment as a new addition to the existing library of data preprocessing methods to improve wind power forecasting performance. A methodological framework of data enrichment is developed with four executable steps: add error features of weather prediction sources, add features of weather prediction at neighboring nodes, add time series features of weather prediction sources, and add complementary weather prediction sources. The proposed data enrichment method takes full advantage of multiple commercially available weather prediction sources and the physical continuity nature of wind. It can cooperate with any existing forecasting models that have weather prediction data as inputs. The controlled experiments on three actual individual wind farms have verified the effectiveness of the proposed data enrichment method: The normalized root mean square error (NRMSE) of the day-ahead wind power forecast of XGBoost and LSTM with data enrichment is 11% to 27% lower than that of XGBoost and LSTM without data enrichment. In the future, variations on the data enrichment methods can be further explored as a promising direction of enhancing short-term wind power forecasting performance.

Keywords: wind power forecasting; data enrichment; data preprocessing; weather prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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