Abstract
Wind power unlike power generated from conventional sources is not constant. There are many factors that influence the power generated from wind energy, like wind speed, location, climate change etc. Owing to this, there is always uncertainty in wind power output. Thus, for proper load scheduling and better integration of wind power with the grid, it becomes essential to develop a robust wind power forecasting system. For developing a reliable forecasting system, it is essential to factor in all the possible factors that affect the wind power output and analyze a huge amount of data set for a higher accuracy rate. This paper proposes the use of two machine learning techniques, namely LASSO and XGBoost classifier, and a comparison is made between the two to find which technique is better for our task. For training and validation of this model, wind power data of the Kolkata region is taken. The result shows that XGBoost is better than LASSO for forecasting wind power accurately with a MAPE value of 1.121 for XGBoost and 62.1476 for LASSO.
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Kumar, A., Kumar, N., Singh, B., Chaudhary, A., Dikshit, K., Sharma, A. (2021). Comparative Investigation of Machine Learning Algorithms for Wind Power Forecasting. In: Singh, J., Kumar, S., Choudhury, U. (eds) Innovations in Cyber Physical Systems. Lecture Notes in Electrical Engineering, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-16-4149-7_46
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