izvorni znanstveni rad
Strategic planning, development, and operation of a renewables-based smart grid require accurate modelling of grid components in regard to prediction of power or energy output in relation to renewable resources. Here we contribute to the subject with the development and validation of methodology for a wind energy plant output prediction model development based on experimental observations of renewables (wind parameters) and deployment of machine learning techniques. Model development methodology does not require component-based models. We developed successfully a model that encompasses more than 90% of the original variability of data from wind power plant observations in northern Spain. The model is intended to be expanded and deployed as a component in the over-all renewables-based smart grid operation model for remote and isolated communities.
renewable; prediction model; wind power; smart grid; machine learning; remote community
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The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…
