Optimal Power Management for Energy Harvesting Systems with A Backup Power Source

Draskovic, Stefan; Thiele, Lothar

doi:10.1109/meco52532.2021.9460139

Cited by 12 publications

(13 citation statements)

References 27 publications

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“…This indirectly covers effects like room geometry, mixtures of different light sources, translation from illuminance to energy intake and solar panel efficiency. This hence enables a more autonomous approach required TABLE I. Overview of the locations with a qualitative description of lighting conditions provided by [17] and [28].…”

Section: Related Workmentioning

confidence: 99%

Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

et al. 2023

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We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of training data and analyze the sources of errors to understand the potential of indoor photovoltaic energy harvesting predictions.INDEX TERMS energy harvesting, photovoltaic systems, energy predictions, machine learning.

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Section: Related Workmentioning

confidence: 99%

Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

et al. 2023

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“…In Ref. [33], this scheduler was implemented on the MSP432 microcontroller for an indoor scenario. The prediction horizon spanned 48 time intervals and an error of ϵ = 0.01 acted as stopping criteria.…”

Section: Case Study: Robustness Analysismentioning

confidence: 99%

“…[33], we do not consider this because in Ref. [33] it was determined to be negligible for a similar system. Subsequently, a simulation is performed with the determined parameters and compared to the measured system behaviour.…”

Section: Experimental Validation: Indoor Harvesting Scenariomentioning

confidence: 99%

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Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

Stricker

Forno

Thiele

2022

IET Computers & Digital Tech

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Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non‐deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting‐based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non‐robust system's performance by up to 13.8 times in a real‐world setting.

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“…Hence, in this case, the reliable energy can serve as backup energy. The design of the coexistence of reliable backup energy and harvested energy has been researched and promoted in academia and industry [ 8 , 11 , 12 , 13 , 14 ]. The mixed energy supply mode can enhance the reliability of the system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Information Update for Energy Harvesting Sensor with Reliable Backup Energy

Wang

Peng

Chen

et al. 2022

Entropy

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The timely delivery of status information collected from sensors is critical in many real-time applications, e.g., monitoring and control. In this paper, we consider a scenario where a wireless sensor sends updates to the destination over an erasure channel with the supply of harvested energy and reliable backup energy. We adopt the metric age of information (AoI) to measure the timeliness of the received updates at the destination. We aim to find the optimal information updating policy that minimizes the time-average weighted sum of the AoI and the reliable backup energy cost. First, when all the environmental statistics are assumed to be known, the optimal information updating policy exists and is proved to have a threshold structure. Based on this special structure, an algorithm for efficiently computing the optimal policy is proposed. Then, for the unknown environment, a learning-based algorithm is employed to find a near-optimal policy. The simulation results verify the correctness of the theoretical derivation and the effectiveness of the proposed method.

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Optimal Power Management for Energy Harvesting Systems with A Backup Power Source

Cited by 12 publications

References 27 publications

Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

Optimal Information Update for Energy Harvesting Sensor with Reliable Backup Energy

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