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To respond to variations in solar energy, harvestedenergy prediction is essential to harvested-energy management approaches. The effectiveness of such approaches is dependent on both the achievable accuracy and computation overhead of prediction algorithm implementation. This paper presents detailed evaluation of a recently reported solar energy prediction algorithm to determine empirical bounds on achievable accuracy and implementation overhead using an effective error evaluation technique. We evaluate the algorithm performance over varying prediction horizons and propose guidelines for algorithm parameter selection across different real solar energy profiles to simplify implementation. The prediction algorithm computation overhead is measured on actual hardware to demonstrate prediction accuracy-cost trade-off. Finally, we motivate the basis for dynamic prediction algorithm and show that more than 10% increase in prediction accuracy can be achieved compared to static algorithm.