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

Shaswot Shresthamali, Masaaki Kondo, Hiroshi Nakamura

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

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
Volume16
Issue number5s
DOIs
Publication statusPublished - 2017 Sep
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

Fingerprint

Dive into the research topics of 'Adaptive power management in solar energy harvesting sensor node using reinforcement learning'. Together they form a unique fingerprint.

Cite this