Abstract

This study evaluates the effectiveness of SHAP (SHapley Additive exPlanations) as a feature selection tool for national demand forecasting, addressing the need for a comprehensive understanding of its strengths and limitations. We benchmark SHAP against common selection methods, including variance-based selection, XGBoost, and NSGA-II. Using a 1D Convolutional Neural Network to forecast hourly national energy demand in Panama, we compare the performance of features selected by each method. Our results indicate that SHAP is generally outperformed by XGBoost and may not be the most effective initial approach for feature selection, nor does it consistently rank features by their predictive value. However, SHAP proves more effective in refining smaller sets of features, maintaining model accuracy by eliminating non-contributive features

Abstract

Shallow geothermal utilization systems (especially GSHP systems) are used for heating and cooling, hot water supply, snow melting, and other applications. They are an energy-saving technology with the potential to decarbonize and reduce utility costs. However, GSHP systems have been slower to spread in Japan than in the other countries due to the high cost of GSHP system installation, lack of awareness, and lack of hydrogeological information. Therefore, the Shallow Geothermal and Hydrogeology Team of AIST is engaged in research on social implementation to popularize shallow geothermal utilization system, and this report introduces an example of such research.

Abstract

With the widespread adoption of Carbon Capture and Storage (CCS) technologies and investigations into advanced power cycles such as the supercritical CO2 (sCO2) Brayton cycle, it is particularly crucial to focus on the risks and dynamics associated with accidental leaks under supercritical conditions. One key aspect is the accurate prediction of critical flow rates of supercritical fluids during leakage events. Owing to the scarcity of experimental data on critical flow in sCO2 microchannels, this study harnesses the abundant experimental data from critical flow studies of water, employing machine learning algorithms enhanced by transfer learning to predict the critical flow rates of sCO2. The approach involves pre-training a neural network on critical flow data from water, followed by fine-tuning with sCO2 data, thereby bridging the gap in data availability and enhancing the model’s generality across various parameters. The principal research findings indicate that transfer learning can improve prediction accuracy and adaptability, suggesting that transferring knowledge from extensively studied fluids like water can effectively enhance the predictive performance of less-documented supercritical fluids. This research provides a valuable tool for designing safer CCS and energy systems.

Abstract

Biogas, produced through the anaerobic digestion (AD) process of biomass, is one of the most promising renewable energy sources. Besides CH4, biogas contains other components that negatively impact its direct application, such as H2S, CO2, NH3, and siloxanes. Among these, CO2 has the most significant effect on reducing the calorific value of biogas, with its content reaching 30%-45%. Therefore, the separation of CO2 from biogas using solid adsorbents for biogas upgrading has been a widely studied topic in recent years. Previous theoretical studies have found strong interactions between Fe3O4 and CO2, indicating a high CO2-specific adsorption capacity. However, the low specific surface area of Fe3O4 limits its CO2 loading capacity. Coating the surface of Fe3O4 with a carbon layer to form carbon-coated magnetite (C@Fe3O4) can significantly increase its specific surface area, making Fe3O4 a promising new CO2 adsorption material. In this study, a novel C@Fe3O4 material was prepared using concentrated organic matter from biogas effluents and iron oxide through a hydrothermal-carbonization process. This material achieved a specific surface area of up to 187.50 m²/g. Under conditions of 1 bar and 37°C, the material demonstrated a selectivity for CO2 over CH4 of up to 10.5, outperforming most adsorbent materials. Its CO2 adsorption capacity was measured at 0.56 mmol/g (0.99 mmol/cm³).This material supports the development of a new, efficient, low-energy, and low-cost pressure swing adsorption (PSA) process for CO2 and CH4 purification.

Abstract

Waste heat recovery, Thermal Energy Storage, Phase Change Materials, Packed Bed Latent Heat Storage Systems, Machine Learning, Harmony Search.

Abstract

Transportation electrification can reduce dependence on fossil fuels and promote energy transition. In the literature, numerous research efforts have been dedicated to investigating the influence of diverse energy and climate policies on EV penetration. However, limited attention has been given to the specific effects of ride-hailing services on EV penetration and the broader sustainable energy transition. In this study, we combine a Stated Preference (SP) survey with a bottom-up transportation model to examine the role of ride-hailing services on EV penetration and energy transition. Our method provides the benefit of analyzing how individuals opt for ride-hailing services over other transportation modes for different trips, while also endogenously determining the technology mix. The results reveal that ride-hailing services have the potential to increase EV penetration by 2.46%, or by 2.89% if the ride is shared, thereby contributing to the transition towards clean energy. Policy interventions such as the implementation of carbon pricing and the promotion of public transport can further drive transportation electrification, showcasing a synergistic effect with ride-hailing services.

Abstract

This paper presents a fuel cell hybrid electric propulsion system (FCHEPS) that integrates hydrogen fuel cell, cryogenic cooling, and superconducting technology, specifically designed for modern twin-engine turboprop regional aircraft. A parametric modeling approach was applied to key components, including hydrogen fuel cells, superconducting motors, and liquid hydrogen tanks. Additionally, a micro-channel plate hydrogen-helium heat exchanger (HX) was developed to maintain the optimal inlet temperature for the fuel cell and to cool cryogenic components. Energy management strategies based on state machine (SM) and equivalent consumption minimization strategy (ECMS) were developed to achieve optimal power distribution. Simulation results across cruise and emergency mission profiles validated the effectiveness of the powertrain configuration, analysis model and energy management strategies. The ECMS method demonstrated minimal fuel consumption and maximum energy conversion efficiency, making it particularly suitable for scenarios with frequent power demand fluctuations.

Abstract

Ammonia-hydrogen hybrid vehicles have emerged in recent years as zero carbon emissions vehicles, garnering significant attention from researchers. Currently, related research is primarily focused on ammonia-hydrogen internal combustion engines, while the overall power configuration and energy management methods for these vehicles remain underdeveloped. Therefore, this paper proposes an ammonia-hydrogen hybrid power system based on ammonia thermal decomposition technology, effectively leveraging hydrogen’s high combustion speed and ammonia’s high energy density. Additionally, a rule-based energy management method for this system was designed and optimized using a breadth-first search (BFS) algorithm, achieving promising results. The proposed energy management method not only provides a valuable reference for related research but also offers practical guidance for engineering applications.

Abstract

Wearable thermoelectric coolers (TECs), applicable in both indoor and outdoor environments, directly cool the human body surface and are considered to have much higher energy efficiency compared to compressor-based air-conditioners. However, the low coefficient of performance (COP) of TECs continues to be a significant burden on lithium-ion batteries, limiting the widespread use of TEC-based wearable coolers. In this study, we propose a novel water-electricity hybrid energy system by utilizing the water evaporation and capillary effect of water to solve the high electricity consumption issue of TECs. The novel water-electricity energy system-based cooler saves 81.8% of the electricity required to reach the lowest cooling temperature of a traditional TEC, by keeping the same size to the TEC. The excellent cooling capability enables the provision of cooling air, significantly increasing the practicality of the cooler compared with conductive models, and substantially contributing to energy conservation in both indoor and outdoor environments.