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This study introduces a novel solar energy prediction model for wireless sensor networks. It accurately forecasts energy availability using the sun

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Area of Science:

  • Energy Harvesting
  • Wireless Sensor Networks
  • Renewable Energy Systems

Background:

  • Solar energy is a key ambient source for wireless sensor networks (WSNs).
  • Solar radiation variability necessitates accurate energy prediction for WSN power management.
  • Existing prediction models often rely on historical energy harvesting data.

Purpose of the Study:

  • To present a novel solar energy prediction model for WSNs.
  • To develop a model that does not require historical energy harvesting data.
  • To enable adaptive energy demand in solar-powered WSNs.

Main Methods:

  • Utilizes the sun's altitude angle at different times of day for prediction.
  • Employs low-complexity operations for energy forecasting.
  • Avoids the need for maintaining local energy harvesting patterns.

Main Results:

  • Achieves accurate solar energy predictions for arbitrary forecasting horizons.
  • Demonstrates high prediction accuracy with minimal computational effort.
  • Shows the model's robustness across different scenarios and locations.

Conclusions:

  • The proposed model offers a simple and effective solution for solar energy prediction in WSNs.
  • Its independence from historical data simplifies deployment and tuning.
  • Enables reliable energy management for solar-powered wireless sensor nodes.