![]() ![]() ![]() ![]() To quantify this trade-off, four machine learning forecast models are tested on two geographical locations for time-steps varying from 2 to 60 min and horizons from 10 min to 6 h, on global irradiance horizontal and tilted when data was available. The forecast time-step is an important parameter affecting not only its accuracy but also the optimal control time discretization, hence its efficiency and computational burden. One of their key components is the forecast of the energy production from very short to long term. With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises.
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