Improving Energy Usage in Energy Harvesting Wireless Sensor Nodes Using Weather Forecast

Battery powered wireless sensor nodes are used in many applications. They can be placed in remote locations and can operate for a long period of time without the need for maintenance. However, finite battery lifetime is one of the main limitations of such devices. Energy harvesting can be used to recharge batteries from environmental energy sources. This enables continuous work when energy neutrality is satisfied. For optimal use of available energy, when energy harvesting is used, the optimization goal switches from energy management to workload maximization while maintaining energy neutrality. In order to achieve energy neutrality, prediction of energy that can be harvested in the future is needed. This prediction can be based on previous measured data. However, this approach can be unreliable when weather conditions change during the day or between days. To improve prediction precision, weather forecast can be used. This information has been used to predict energy that can be harvested in the future but only for the next few hours. We present a two-level predictor that uses cloud cover information from hourly weather forecast for next 24 hour period to predict energy that can be harvested in the same time interval. Proposed predictor achieves a 26% less prediction mean absolute percentage error, a 15% less mean absolute deviation percent error and allows an 8% better performance of simulated wireless sensor node compared to Exponentially Weighted Moving Average (EWMA) predictor