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Smart city article details
| Title | Short-Term Wind Speed And Power Forecasting For Smart City Power Grid With A Hybrid Machine Learning Framework |
|---|---|
| ID_Doc | 48718 |
| Authors | Wang Z.; Wang L.; Revanesh M.; Huang C.; Luo X. |
| Year | 2023 |
| Published | IEEE Internet of Things Journal, 10, 21 |
| DOI | http://dx.doi.org/10.1109/JIOT.2023.3286568 |
| Abstract | To address foreseeable challenges during the penetration of wind energy into the power grid, including accurate wind power forecasting and smart power generation scheduling, this study proposes a novel short-term wind speed forecasting model, named EMD-KM-SXL, which is based on empirical mode decomposition (EMD), $K $ -means clustering and machine learning, and a new two-stage short-term wind power forecasting model based on wind speed forecasting and wind power curve (WPC) modeling. The former wind speed forecasting model regards historical wind speed observations as model input and the latter power forecasting model utilizes knowledge augmentation, introducing wind power conversion relationship, environmental factors as well as wind power system status parameters. In the proposed wind speed forecasting model, three machine learning models, including support vector regressor, XGBoost regressor, and Lasso regressor, are employed to forecast three types of frequency components that generated via EMD and $K $ -means clustering. Then, the WPC model is utilized to compute potential outpower based on wind speed prediction value speed, which is regarded as the first stage of the proposed wind power forecasting model. In the second stage, environmental factors and wind power system status parameters are introduced and an artificial neural network model, considering preliminary predicted power, environmental factors, and wind power system status parameters as model input is built to make final power prediction. Computational results show that the proposed models achieve the best performance in terms of wind speed and power forecasting on different forecasting horizons ranging from 10 to 40 min, compared with benchmarking methods. © 2014 IEEE. |
| Author Keywords | Data driven; knowledge augmentation; smart city; wind power forecasting |
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