Abstract
Wind energy is becoming one of the most important suppliers of renewable energy but due to its reliance on weather conditions it is highly inconsistent and its integration into electricity grids is a challenge. In this research we present a comparative analysis of the performance of several prominent data mining techniques in prediction of wind energy generation. Data from the Big Data Challenge Bremen 2018 was used for short term forecasting. Of basic models, a decision tree produced the best performing model. It performed marginally better than SGD, OLS, LASSO and Bayesian ridge regression. Whereas, SVM, nearest neighbor and Gaussian NB performed very poorly. A further analysis using ensemble methods was performed where a Gradient Boosting was the best model. Further improvements of the IoT model are performed and limitations of this are discussed in detail.
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Khalid, M., Khan, M.B., Dad, I., Fateh, S. (2022). IoT-Based Data Driven Prediction of Offshore Wind Power in a Short-Term Interval Span. In: Hussain, W., Jan, M.A. (eds) IoT as a Service. IoTaaS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-030-95987-6_17
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