Adaptive power management in solar energy harvesting sensor node using reinforcement learning

Shaswot Shresthamali, Masaaki Kondo, Hiroshi Nakamura

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)


In this paper, we present an adaptive power manager for solar energy harvesting sensor nodes. We use a simplified model consisting of a solar panel, an ideal battery and a general sensor node with variable duty cycle. Our power manager uses Reinforcement Learning (RL), specifically SARSA(λ) learning, to train itself from historical data. Once trained, we show that our power manager is capable of adapting to changes in weather, climate, device parameters and battery degradation while ensuring near-optimal performance without depleting or overcharging its battery. Our approach uses a simple but novel general reward function and leverages the use of weather forecast data to enhance performance. We show that our method achieves near perfect energy neutral operation (ENO) with less than 6% root mean square deviation from ENO as compared to more than 23% deviation that occur when using other approaches.

Original languageEnglish
Article number181
JournalACM Transactions on Embedded Computing Systems
Issue number5s
Publication statusPublished - 2017 Sept
Externally publishedYes


  • IoT
  • Power management
  • Reinforcement learning
  • Wireless sensor nodes

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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