Abstract

A model of EV charging at work or on the go and EV discharging at home (V2H home discharging only) was used to estimate residential energy consumption for two types of hot water heating: gas and electric (heat pump) systems, in a model assuming an average house with a couple and three children and no energy-saving measures (baseline) using the Architectural Institute of Japan’s standard housing model BEST-H. It was found that the operation of the V2H home discharging only model is an effective energy-saving measure for existing houses without energy-saving measures where it is difficult to install solar panels, and therefore it is desirable for it to be widespread. In addition, since gas hot water is often used in existing houses, the implementation of carbon recycling technology is expected to enable further CO2 reduction and accelerate the shift to zero carbon.

Abstract

This study investigates the techno-economic performance of hybrid renewable energy systems integrating PV, battery storage, and green hydrogen storage for conventional and passive residential buildings. Using TRNSYS simulations, parametric cases were evaluated to explore cost-self-sufficiency trade-offs across three system configurations: PV-only, PV-Battery, and PV-Battery-Hydrogen. Pareto-optimal solutions were identified based on self-sufficiency ratio (SSR) and annualized system cost. The results showed that incorporating hydrogen systems significantly improved SSR with modest incremental system cost relative to battery-only systems. Although high SSR was achievable with battery storage alone, it required extremely large capacities and costs. In the knee-point analysis, passive buildings—due to higher surplus energy—reduced long-term storage needs, enabling hydrogen system downsizing and achieving 97.2% SSR at lower annualized cost than the conventional buildings (80.3% SSR). These findings underscore that adopting hydrogen systems enables economically viable pathways to high energy self-sufficiency, with passive design further enhancing performance while minimizing system costs in residential buildings.

Abstract

In smart city low-voltage distribution networks, the proliferation of residential microgrids, EV charging stations, and distributed generation sources induces significant three-phase imbalances in load, current, and voltage. These imbalances degrade power quality, reduce equipment utilization, and compromise system stability. Current phase identification methodologies rely on manual on-site inspections by utility personnel, entailing high labor expenditures, prolonged downtime, and operational inefficiencies. To this end, we propose a Smart City-Oriented Phase Identification method leveraging customer-side voltage data analysis. First, Principal Component Analysis (PCA) is leveraged to reduce dimensionality and extract salient features from voltage time-series data. Subsequently, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is innovatively integrated to cluster the PCA-derived feature vectors, enabling accurate inference of user-phase affiliations. Field validation conducted at a secondary substation of a local power supply bureau demonstrates real-time computational efficiency and achieves phase identification accuracy exceeding 90%.

Abstract

The revised 2024 Energy Performance of Buildings Directive (EPBD) has strengthened the use of the Energy
Performance Certificate (EPC) as the key tool for assessing and classifying the energy efficiency of buildings. Heterogeneity persists, however, in how ratings are interpreted, implemented and made accessible, within and across EU countries. Significant challenges still exist in terms of cross-country alignment, with capabilities of AI and ML for global region analysis underexploited, and research overlaps common. This paper investigates ways in which the Colouring Cities Research Programme (CCRP), and its network of research-led, open spatial data visualisation platforms, can support wider and more consistent EPC implementation, and public dialogue around energy targets. Drawing on case studies from Sweden and Britain, it compares methods for generating energy ratings; EPC openness and accessibility; and highlights the benefits of publicly visualising EPCs at multiple scales using a shared data schema. Results show the value of interactive and integrated open platform networks, for mapping and contextualising EPC data, increasing crosssector engagement, and helping nudge behaviour to accelerate decarbonisation in the building sector.

Abstract

In response to climate change, building design is increasingly shifting its focus toward thermal performance and internal energy flows. Regarding the space as a dynamic system, this paper approaches the characteristics of its thermal behavior through dominant modes. An automated analysis framework is introduced, applying Dynamic Mode Decomposition to the temperature field of open spaces based on multi-zone simulation, followed by a Generative Adversarial Network modeling to speed up the feedback. The predicted modes reveal the organization of energy flows, offering insights to early design considerations such as thermal zoning and partition layout. The workflow is developed on the Rhino-Grasshopper platform, utilizing EnergyPlus as the simulation engine and pix2pix as the network model.

Abstract

Many municipalities worldwide are setting net-zero emission targets. In Japan, over 1,000 of 1,791 municipalities, representing 99.7% of the population, have committed to achieving net zero by 2050. Despite these desirable commitments, most municipalities still lack a clear vision or concrete solutions for reaching their goals. To help local decision-makers develop effective decarbonization plans, it is crucial to identify municipalities with similar energy situations. This study aims to categorize municipalities using regional energy demand data. To effectively cluster municipalities based on energy demand, both the total energy volume and the composition of energy sectors were considered. We employed a Compositional Data Analysis (CoDA) approach to adequately incorporate both volume and sector composition information. While still rare in energy-related studies, the CoDA approach combined with both relative and absolute information provides a robust framework for classifying municipalities. From the results, we obtained intuitive and interpretable clusters for tacking the issue.

Abstract

To realize a decarbonized community, it is increasingly important for local governments and various other regional entities to introduce and expand local production of local consumption-type energy systems. We conducted a case study on the regional energy supply using a local energy planning and evaluation system we are developing to promote such localized consumption of energy. Specifically, using actual supply data from local power companies in Nose Town and Toyono Town, reductions in primary energy consumption were calculated by assuming scenarios in which renewable energy and energy management technologies were introduced. The evaluated scenarios included the use of conventional equipment, distributed regional energy supply, the adoption of renewable energy and local power producers and suppliers (PPS), the use of mobility devices such as EVs for energy storage, and optimal control of storage batteries. The results indicate that the introduction of renewable energy can significantly reduce the electricity supplied from grid power outside the area. However, since the actual reduction effects naturally depend on the assumed scale of implementation, evaluation cases based on various scenarios should be accumulated and knowledge should be standardized in the future.

Abstract

In recent years, the development of woody biomass power generation facilities certified under the Feed-in Tariff (FIT) and Feed-in Premium (FIP) schemes has rapidly progressed across various regions. However, large-scale woody biomass power generation without thermal utilization has been criticized for significant energy losses and the unsustainable demand for fuelwood that exceeds the capacity of local forestry sectors. In response, increasing attention is being directed toward Intra-Regional Ecosystem initiatives that promote the local circulation of forest resources through thermal utilization of woody biomass. This study aims to assess the feasibility of a locally produced and consumed heat utilization system using woody biomass in mountainous rural cities. Specifically, it evaluates, from an economic perspective, the potential for efficiently collecting residual forest biomass using drones to meet the heat demand of small settlements. Simulation results suggest that in a settlement of approximately 25 households, forest residues generated within a 500-meter radius can be collected and chipped using drones and supplied at a lower cost than commercially available wood chips. Moreover, compared to systems that rely on trucks and forwarders, drone-based collection systems can offer more stable cost performance regardless of forest terrain conditions.

Abstract

This study proposes a physics-informed feature selection framework to enhance the accuracy and interpretability of data-driven cooling load prediction models. Using operational data from a metro station HVAC system, we first identify temporal features through Pearson correlation analysis, followed by thermodynamic consistency screening to eliminate physically implausible candidates. Mutual information (MI) is then employed to quantify feature importance, while our Synergistic approach strategically incorporates physically critical but lower-MI features to improve model accuracy. Tested with LSTM models, the proposed method achieves a 5.6% improvement in prediction accuracy and 16-23% error reductions compared to conventional MI-based selection, with negligible computational overhead. Further validation with BPNN and diverse datasets confirmed robustness. By bridging data-driven metrics and domain-specific physics, this approach offers a balanced trade-off between accuracy and interpretability for building energy prediction.

Abstract

“Carbon Recycling Technology” is one of the most focused exhibits showcased under the concept of the “Green Expo” promoted by the Osaka-Kansai Expo 2025. This technology has been positioned as essential for achieving a carbon-neutral society in Japan’s Basic Energy Plan formulated in 2025 and is expected to become a crucial component of the energy systems in future cities.