A dynamic data-driven forecast prediction methodology for photovoltaic power systems

Kapros, Zoltán (2018) A dynamic data-driven forecast prediction methodology for photovoltaic power systems. IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE, 122 (3). pp. 345-360. ISSN 0324-6329

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Abstract

At present, the capacity of the new photovoltaic (PV) systems are growing rapidly in Hungary. The limit to growth can be estimated, but it is influenced by several things. Even a realistic goal for the next 20–30 years can be to reach the 20–25% variable renewable energy ratio in the electricity consumption. The main barrier is the variability of these systems, thus the grid integration is a huge challenge in the near future. A new dynamic data-driven forecasting methodology is worked out and tested by examining the Budapest District Heating Co. Ltd. top installed solar systems. The tested prediction method was only for 5 minutes ahead in the expected average performance in a 15-minute period. The main elements of the tested methodology and some main results will be presented in this article.

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