  Solar based electricity generations have experienced strong and impactful
growth in recent years. The regulation, scheduling, dispatching, and unit
commitment of intermittent solar power is dependent on the accuracy of the
forecasting methods. In this paper, a robust Expanded Extreme Learning Machine
(EELM) is proposed to accurately predict solar power for different time
horizons and weather conditions. The proposed EELM technique has no randomness
due to the absence of random input layer weights and takes very less time to
predict the solar power efficiently. The performance of the proposed EELM is
validated through historical data collected from the National Renewable Energy
Laboratory (NREL) through various performance metrics. The efficacy of the
proposed EELM method is evaluated against basic ELM and Functional Link Neural
Network (FLNN) for 5 minutes and 1 hour ahead time horizon.
