Adaptive Power Management in Solar Energy Harvesting Sensor Node Using Reinforcement Learning
Adaptive Power Management in Solar Energy Harvesting Sensor Node Using Reinforcement Learning
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DOI：
10.1145/3126495
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发表时间：
2017-09
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影响因子：
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通讯作者：
Shaswot Shresthamali;Masaaki Kondo;Hiroshi Nakamura
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文献类型：
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作者：
Shaswot Shresthamali;Masaaki Kondo;Hiroshi Nakamura
在本文中，我们提出了一个自适应的太阳能收集传感器节点的电源管理器。我们使用一个简化的模型，由一个太阳能电池板，一个理想的电池和一个一般的传感器节点与可变占空比。我们的电源管理器使用强化学习（RL），特别是SARSA（λ）学习，从历史数据中训练自己。经过培训后，我们证明我们的电源管理器能够适应天气、气候、设备参数和电池退化的变化，同时确保接近最佳的性能，而不会耗尽或过度充电。我们的方法使用了一个简单但新颖的一般奖励函数，并利用天气预报数据来提高性能。我们表明，我们的方法实现了接近完美的能量中性操作（ENO）与ENO的均方根偏差小于6%相比，超过23%的偏差，使用其他方法时发生。
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.