ASARIMA: An Adaptive Harvested Power Prediction Model for Solar Energy Harvesting Sensor Networks

Abstract

Harvesting energy from solar radiation has emerged as an effective approach to prolong the lifetime of outdoor energy harvesting sensor networks. The harvested energy must be carefully managed to ensure that sufficient energy is available when solar energy is scarce. For the prediction problem of solar energy power harvesting, this paper proposes an adaptive seasonal auto-regressive integrated moving average model (ASARIMA) for solar energy harvesting prediction. A training set can be adaptively adjusted by the similarity of historical data, and then we conduct seasonal difference data fitting based on the adjusted training set to obtain the optimal prediction model parameters. Experimental results show that this ASARIMA model performs better than other existing power prediction algorithms. If the weather conditions are stable, the prediction error of the ASARIMA decreases by more than 70%. If the weather conditions change sharply, the prediction error decreases by more than 20% in comparison with those of other algorithms.