engleski

Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging

This paper proposes forecasting methods for Wind Power Plants` (WPPs`) generation based on Machine Learning Algorithms. In order to increase the precision of generation forecasts from WPPs further, the methodology is introduced of organising WPP owners in a so-called "ECO balance group”. The described theoretical bases have been applied to the WPPs in the Croatian transmission power system. Deviation calculations were made for the forecasted and realised generation of WPPs in 2015, when the proposed methods were not effective, and for 2019 when these methods had already been applied.

Machine Learning ; power system ; forecasting methods ; regulatory frameworks ; Wind Power Plants.

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