Yibo Wang

Guangzhou University

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Y.J. Lee

Industrial Technology Research Institute

Ming‐Jer Kao

National Chung Hsing University

Youshuang Zhang

Guangzhou University

Jiunn Yih Lee

Industrial Technology Research Institute

Wanke Chen

Industrial Technology Research Institute

Jia Liu

Peking University

S. Chao

National Tsing Hua University

Sicheng Wan

Southwest University

C. C. Hung

ITRI International

Xinman Chen

South China Normal University

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Industrial Technology Research Institute

ITRI International

National Yang Ming Chiao Tung University

Guangzhou University

National Central University

Taiwan Semiconductor Manufacturing Company (Taiwan)

National Taiwan University

Ningbo University

National Tsing Hua University

South China Normal University

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Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction

Sustainability (2024)

Sicheng Wan Yibo Wang Youshuang Zhang Beibei Zhu Huakun Huang

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.

10.3390/su16166903

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SAF exhibits better resistance to roughness edge than single-free layer

Journal of Magnetism and Magnetic Materials (2006)

Y.J. Lee J.-G. Zhu Ming‐Jer Kao Yibo Wang M.‐J. Tsai

Free surface

Free field

10.1016/j.jmmm.2006.10.1101

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High density and low power design of MRAM

C. C. Hung Ming‐Jer Kao Y.S. Chen Yibo Wang Hong-Hui Hsu

MRAM structures based on 1T2UMTJ cell and PWWL architecture are proposed to shrink the bit size with a potential down to 6 F/sup 2/ by a so-called ExtVia process and reduce the writing current by a factor of two, combined with the nature of non-volatility and high speed, making the MRAM suitable for universal memory applications.

Magnetoresistive random-access memory

Non-Volatile Memory

10.1109/iedm.2004.1419225

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Citations (9)

Improved Whale Optimization Algorithm towards Precise State-of-charge Estimation of Lithium-ion Batteries via Optimizing LSTM

Energy (2024)

Sicheng Wan Haojing Yang Jinwen Lin Junhui Li Yibo Wang

State of charge

Optimization algorithm

10.1016/j.energy.2024.133185

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Citations (4)