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Preliminary work involved using the heart rate variability (HRV) index, specifically the LF/HF value, for real-time thermal comfort assessment.
Recommended citation: Wang Z, Matsuhashi R. Research on thermal comfort by analyzing LF/HF value and heat flow rate. エネルギー・資源学会論文誌 2019;40:154–9. https://doi.org/10.24778/jjser.40.5_154.
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This paper introduces the concept of Relative Thermal Sensation (RTS) and presents a preliminary investigation of its assessment method using an intrusive approach. Further discussions on this topic can be found in our subsequent paper titled “Intrusive and non-intrusive early warning systems for thermal discomfort by analysis of body surface temperature.”
Recommended citation: Wang Z, Onodera H, Matsuhashi R. Proposal of relative thermal sensation: Another dimension of thermal comfort and its investigation. IEEE Access 2021;9:36266–81. https://doi.org/10.1109/ACCESS.2021.3062393.
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This paper introduces a real-time thermal comfort assessment method based on the analysis of heart rate variablity (HRV). The heartbeat data were measured using a portable, non-intrusive, and low-cost wearable heartbeat sensor.
Recommended citation: Wang Z, Matsuhashi R, Onodera H. Towards wearable thermal comfort assessment framework by analysis of heart rate variability. Build Environ 2022:109504. https://doi.org/10.1016/j.buildenv.2022.109504.
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This paper delves further into the concept of Relative Thermal Sensation (RTS) and explores both its intrusive and non-intrusive assessment methods by analysis of body surface temperature using machine learning. Additionally, this paper proposes a novel composite thermal comfort model that can predict an individual’s current thermal comfort (hot, cold, or cozy) and the current thermal sensation transitioning trend (hotter, colder, or no change). Preliminary work on this topic can be found in our earlier paper titled “Proposal of relative thermal sensation: Another dimension of thermal comfort and its investigation.”
Recommended citation: Wang Z, Matsuhashi R, Onodera H. Intrusive and non-intrusive early warning systems for thermal discomfort by analysis of body surface temperature. Appl Energy 2023;329:120283. https://doi.org/10.1016/j.apenergy.2022.120283.
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This paper introduces a novel approach to estimating vehicular fuel consumption and CO₂ emissions by incorporating driving behavior data such as speeding, sudden accelerating, and braking, along with driving time and distances on various road types. Using random forest regression, the study demonstrates the relevance of these behaviors for monthly CO₂ emission estimation and proposes a generalizable model with high prediction accuracy. This research offers insights into CO₂ emission reduction and energy conservation in road transportation.
Recommended citation: Wang Z, Mae M, Nishimura S, Matsuhashi R. Vehicular fuel consumption and CO₂ emission estimation model integrating novel driving behavior data using machine learning. Energies 2024, 17, 1410. https://doi.org/10.3390/en17061410
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This paper presents an innovative framework for day-ahead electricity price forecasting (DAEPF) using a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model to predict prices in Kyushu, Japan. It addresses the challenge of zero prices during high RES periods by incorporating a natural logarithm transformation and a novel “policy-versus-policy” strategy. The CNN–LSTM model outperforms a traditional LSTM in both accuracy and computation time. By integrating multimodal features, the model achieves high prediction accuracy, highlighting the benefits of a comprehensive approach to DAEPF.
Recommended citation: Wang Z, Mae M, Yamane T, Ajisaka M, Nakata T, Matsuhashi R, Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration. Energies 2024, 17, 2687. https://doi.org/10.3390/en17112687
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This paper presents a novel day-ahead electricity price forecasting approach that emphasizes the accurate prediction of extreme prices by introducing two original custom weighted MAE loss functions: one for high-price forecasting and one for low-price forecasting. Integrated with a CNN-LSTM ensemble framework and multimodal inputs for the Japanese electricity market, the method highlights improved forecasting of price spikes and near-zero prices compared with the conventional MAE-based approach, showing practical value for market participants’ trading and decision-making.
Recommended citation: Wang Z, Mae M, Yamane T, Ajisaka M, Nakata T, Matsuhashi R, Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-ahead Electricity Prices Using CNN-LSTM and Ensemble Learning. Energies 2024, 17, 2687. https://doi.org/10.3390/en17112687
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Traditional wearable coolers usually use thermoelectric coolers (TECs) to cool the human body surface in a contact-based manner, i.e., the device has to contact the skin surface directly. This paper utilizes a novel low-energy water-electricity hybrid energy system to power TECs to gain high cooling performance to construct a wearable air-conditioner to be able to provide cooling air.
Recommended citation: Wang Z, Matsuhashi R. Low-energy miniature wearable air-conditioner with direct cold-air delivery: A novel water–electricity hybrid energy system. Build Environ 2026:114590. https://doi.org/10.1016/j.buildenv.2026.114590.
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An introduction to Ziyang Wang’s General Yin-Yang Philosophy, derived from Bruce Lee’s Jeet Kune Do (JKD) martial arts theory, offers practical, feasible methodologies for solving real-world problems in both research and daily life.
Short description of portfolio item number 1
Short description of portfolio item number 2
Published:
Preliminary work involved using the heart rate variability (HRV) index, specifically the LF/HF value, for real-time thermal comfort assessment.
Published:
Preliminary work involved using the heart rate variability (HRV) index, specifically the LF/HF value, for real-time thermal comfort assessment. More information here
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Preliminary work explored both intrusive and non-intrusive method for Relative Thermal Sensation (RTS) assessment.
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Preliminary research focused on the realization of low-energy wearable coolers. More information here
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Preliminary work focused on day-ahead electricity spot price forecasting using Long Short-Term Memory (LSTM).
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Preliminary work focused on day-ahead electricity spot price forecasting using Long Short-Term Memory (LSTM).
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Day-ahead electricity price forecasting using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM).
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Using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) for day-ahead electricity price forecasting and insights for the prediction interval (PI) estimation.
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Pilot Study on a Low-Energy Wearable Air-Conditioner Based on A Novel Water-Electricity Hybrid Energy System.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.
Desription: This work aims to develop a heat sink that utilizes carbon nanotubes (CNTs) to achieve higher heat conductivity compared to traditional aluminum-based heat sinks of the same volume and weight.
Desription: This research presents innovative frameworks for day-ahead electricity price forecasting (DAEPF) using deep learning models to predict prices for the all nine areas nationwide in Japan.
Desription: This work aims to develop a low-energy, small-scale wearable air-conditioner that charges significantly faster than traditional thermoelectric cooler (TEC)-based models. Unlike traditional TEC-based models, which have limited cooling capabilities and can only offer conductive cooling, our air conditioner provides cooling air. Producing cooling air requires a much lower cooling temperature for the cooling part, allowing air to flow through it and cool down sufficiently. Achieving such temperatures typically demands high electricity consumption for traditional TEC-based coolers, a load that Li-ion batteries cannot sustain. Compared to traditional TEC-based models, our device only requires less than 1/5 of the power consumption to achieve the same maximum cooling temperature, significantly increases the practicability of the device. 
