A novel solar energy predictor for communicating sensors

Abstract

Solar energy harvesting constitutes an attractive solution to provide energy for communicating objects. The advantage of this energy over other forms of renewable energy is that the available solar power can be predicted with reasonable accuracy, which allows the implementation of efficient power management techniques. Nevertheless, harvested solar energy comes from a non-controllable source. Therefore, the prediction of the solar radiation and energy availability is a critical issue, as the amount of the harvested energy may vary over time. In this study, a novel solar radiation and energy predictor (Solar Energy Predictor for Communicating Sensors: SEPCS) is proposed. This prediction model uses past energy observations to forecast future energy availability in short term. To assess the performance of the proposed algorithm, authors used database providing the solar radiation evolution for one year. Then, a comparative performance evaluation shows that the SEPCS predictor significantly outperforms the state-of-the-art energy predictors, by decreasing the average prediction error from 28 to 6.5%.