Frequency adjustable energy harvesting for autonomous wireless sensor nodes

Zhou, Ke; Jin, Qingren; Wang, Xiaoming; Bao, Feng; Sun, Hongdi; Teng, Bing; Ma, Jian

doi:10.1117/12.2642583

Cited by 9 publications

(15 citation statements)

References 6 publications

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“…Given the prevailing preference for transformer-based models in time series forecasting, this study strongly recommends exploring autocyclic within optimized transformer models such as informer [42], fedformer [43], or autoformer [38]. Such exploration aims to establish superior benchmarks, emphasizing the crucial role of autocorrelation in enhancing the effectiveness of autocyclic.…”

Section: E Testing On a Different Modelmentioning

confidence: 99%

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

Arthur,

Yudistira,

Dewi

2024

IEEE Access

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Time series prediction, a pivotal component in various domains, faces the intricate task of capturing complex patterns and dependencies within sequential data. Autocyclic Learning Rate (Auto-Cyclic) emerges as a groundbreaking solution, seamlessly integrating cosine cyclic learning rates with a meticulous emphasis on autocorrelation and variance. In the realm of time series forecasting, where longterm dependencies and dynamic patterns pose significant challenges, AutoCyclic represents a transformative approach. By dynamically adjusting learning rates based on the nuanced characteristics of time series data, AutoCyclic not only addresses issues such as underfitting and overfitting, but also provides exceptional robustness to outliers. Through rigorous evaluations on diverse datasets, including ETTm2, M4, and WindTurbine, AutoCyclic consistently outperforms traditional optimizers such as Adams Optimizer and Cosine Cyclic Learning Rate, showcasing the lowest Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values. What sets AutoCyclic apart is not only its predictive accuracy, but also its astute exploitation of autocorrelation, which provides a unique advantage in discerning and leveraging inherent patterns in time series data. This intrinsic capability positions AutoCyclic as a transformative tool, redefining the landscape of time series forecasting methodologies and opening new dimensions in the utilization of autocorrelation for more insightful and accurate predictions.

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Section: E Testing On a Different Modelmentioning

confidence: 99%

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

Arthur,

Yudistira,

Dewi

2024

IEEE Access

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“…The resulting products are still being used by many power companies today. Later, a large body of literature has developed and applied deep learning models including LSTM-RNN [21], RBM [31], CNN [57] and more recently, transformer-based network [10,60]. Despite the strong fitting power, deep learning architectures often suffer from overfitting due to multiple layers [46] and not well-interpretable to human.…”

Section: Related Workmentioning

confidence: 99%

Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting

Yang

Ren

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Electric load forecasting is an indispensable component of electric power system planning and management. Inaccurate load forecasting may lead to the threat of outages or a waste of energy. Accurate electric load forecasting is challenging when there is limited data or even no data, such as load forecasting in holiday, or under extreme weather conditions. As high-stakes decision-making usually follows after load forecasting, model interpretability is crucial for the adoption of forecasting models. In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry for improved performance. This boosting-based GAM leverages piecewise linear functions and can be learned through our efficient algorithm. In both public benchmark and electricity datasets, our interactive GAM outperforms current state-of-the-art methods and demonstrates good generalization ability in the cases of extreme weather events. We launched a user-friendly web-based tool based on interactive GAM and already incorporated it into our eForecaster product, a unified AI platform for electricity forecasting. CCS CONCEPTS• Computing methodologies → Machine learning algorithms.

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“…2022 and FEDformer Zhou et al. 2022), and popular ones (e.g., TCN Bai et al. 2018 and LSTM Hochreiter and Schmidhuber 1997); (2) Traditional machine learning models such as CatBoost Dorogush et al.…”

Section: Eforecaster Platformmentioning

confidence: 99%

Energy forecasting with robust, flexible, and explainable machine learning algorithms

Zhu,

Chen,

Xia

et al. 2023

AI Magazine

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Energy forecasting is crucial in scheduling and planning future electric load, so as to improve the reliability and safeness of the power grid. Despite recent developments of forecasting algorithms in the machine learning community, there is a lack of general and advanced algorithms specifically considering requirements from the power industry perspective. In this paper, we present eForecaster, a unified AI platform including robust, flexible, and explainable machine learning algorithms for diversified energy forecasting applications. Since October 2021, multiple commercial bus load, system load, and renewable energy forecasting systems built upon eForecaster have been deployed in seven provinces of China. The deployed systems consistently reduce the average Mean Absolute Error (MAE) by 39.8% to 77.0%, with reduced manual work and explainable guidance. In particular, eForecaster also integrates multiple interpretation methods to uncover the working mechanism of the predictive models, which significantly improves forecasts adoption and user satisfaction.

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Frequency adjustable energy harvesting for autonomous wireless sensor nodes

Cited by 9 publications

References 6 publications

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting

Energy forecasting with robust, flexible, and explainable machine learning algorithms

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