Abstract
Sunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ solar prediction models to effectively adapt their energy demands to harvesting dynamics. We present in this paper a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. Unlike most of the state-of-the-art predictors that use past energy observations to make predictions, our model does not require one to maintain local energy harvesting patterns of past days. Performance evaluation shows that our scheme is able to provide accurate predictions for arbitrary forecasting horizons by performing just a few low complexity operations. Moreover, our proposal is extremely simple to set up since it does not require any particular tuning for each different scenario or location.
Highlights
Energy harvesting (EH) is the process that enables the conversion of ambient energy present in the environment into electrical energy for use in powering small, autonomous devices
We evaluated the performance of Pro-Energy, Universal Dynamic WCMA (UD-WCMA), and Solar Altitude Angle (SAA)
The UD-WCMA technique is able to slightly improve the predictions given by Pro-Energy with the low overhead settings (Pro-Low) only, but its estimations are much less accurate than those obtained with our proposal
Summary
Energy harvesting (EH) is the process that enables the conversion of ambient energy present in the environment (e.g., solar, wind, thermal, or kinetic energy) into electrical energy for use in powering small, autonomous devices. Evaluation results using real datasets show that our proposal is able to provide accurate predictions at both short-term and medium-term forecasting horizons by only executing a few low complexity operations Since it does not maintain locally collected past energy measures, the SAA model has a very low memory overhead. The SAA model can be used without requiring any particular configuration in different solar energy harvesters under varying weather conditions and locations. All these features make our proposal suitable for hardware constrained devices (for example, sensor-like motes) that must work under severe resource restrictions such as limited battery and computing power, and/or scarce memory [17].
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