特集：エネルギー効率の進展——設計・技術・政策

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:省エネ設計最適化・蓄熱モデリング・便益評価の最新動向を横断的に把握できる。

🏢実務担当者:ZEB改修や蓄熱・PV冷却技術の導入判断、省エネ投資の複合的便益の説明材料に活用できる。

🏛政策担当者:省エネ政策が技術普及と便益認識にどう影響するか、地域差を踏まえた制度設計の示唆を得られる。

Policy frameworks, regulatory incentives and financial mechanisms are essential in driving the transition to innovative technologies, shaping how societies manage their energy usage. These policies encompass a variety of tools and frameworks to overcome the key barriers and challenges and ensure that energy efficiency practices deliver widespread and lasting benefits.Energy efficiency is a broad and multidisciplinary field which spans a wide range of topics such as analysis and modeling, energy management and performance optimization, policies, innovative technologies. This special issue explores the latest advancement in energy efficient innovations, supportive energy policies, regulatory frameworks, public awareness and demand-side initiatives highlighting their contributions to lowering energy costs for consumers and businesses, reducing dependency on fossil fuels and minimizing greenhouse gas emissions.The contribution provides a comprehensive review of multi-objective optimization (MOO)-based design of cost-effective renovations and retrofit of buildings to achieve net-zero energy buildings (NZEB) or zero-energy buildings (i.e. buildings that consume as much energy as they produce locally from renewable sources) while maximizing indoor thermal comfort, air quality and natural light for occupants. The research aims to identify several building performance objectives that bridge the social, environmental and economic criteria of sustainability. In addition, this research aims to identify the key factors and their assessment criteria to enhance occupant well-being by maintaining a healthy and comfortable indoor environment. It also demonstrates the effectiveness of MOO and its potential for decisionmaking in NZEB development based on a wide range of design variables and under various conditions. The article presents a comparative study of various optimization algorithms, tools, and simulation software in terms of their reliability and computational effectiveness in solving building problems. Finally, this research proposes a four-step hierarchical framework based on MOO to guide building professionals in the design of NZEB. The research is validated through case studies developed for India context.The second contribution focuses on the cooling of photovoltaic (PV) systems in the Sub-Saharian Africa known for its intense solar irradiance and prolonged hours of high temperatures throughout the year which can reduce the efficiency of the PV panels. The article presents a comprehensive coverage of the recent literature on cooling techniques and their performance including active cooling such as photovoltaic-thermal (PV-T) systems which use heat exchangers and fluid circulation and passive cooling such as photovoltaic-phase change material (PV-PCM) which use special materials that melt and solidify at specific temperature without circulating fluids or heat exchangersThe PV-T integrated with phase change material (PV-T/PCM) uniformly regulates PV cell temperature to an optimal operating condition and is considered in this study. A critical design criterion for effective PCM integration in PV-T systems requires that the phasetransition temperature matches the optimal operating temperature range for photovoltaic cells, typically between 30°C and 45 °C. Shea butter, a bio-based PCM that is environmentally sustainable and eco-friendly as compared to the traditional paraffin wax, was selected for this study due to its latent heat characteristics and melting temperature (approximately 32 °C), which aligns exactly with the optimal temperature range.The PV-T/PCM computational models, developed using TRNSYS software, were used to simulate the electrical and thermal behaviour of the hybrid PV-T/PCM system, to analyse the heat-transfer mechanisms and assess the impact of the PCM on the efficiency of the PV system. The models were successfully validated against experimental and simulation studies from the literature. The modelling framework developed in this study can be adapted to a broad spectrum of bio-based PCMs possessing appropriate thermos-physical properties.The contribution focuses on thermal energy storage (TES) in packed granular beds considered as a highly efficient technology for high temperature solar thermal plants, industrial waste heat recovery and grid-scale energy management. This technology stores heat by passing a heated fluid through a stationary bed of solid particles like rocks, ceramics of manufactured sand (M-sand) during the storage phase and reversing the fluid flow to retrieve the heat during the release phase.This research developed a physics-informed neural network (PINN) for predicting the full three-dimensional (3D) temperature distribution inside an M-sand packed-bed thermal energy storage unit. The physical laws governing fluid dynamics and heat transfer are embedded into the NN model's cost function.The PINN was trained on temperature datasets collected from five thermocouples at three distinct volumetric flow rates (1.00, 1.88, and 2.66 LPM), with the governing threedimensional transient heat conduction equation enforced simultaneously through a physicsconstrained loss term evaluated at 300 collocation points.The continuous 3D temperature gradient predicted by the PINN revealed several internal thermal characteristics as compared to the five-sensor experiment which only captures localized temperature measurements. The PINN simultaneously inferred effective physical parameters-thermal conductivity, density-specific heat product, and heat transfer coefficient-from the training process, without separate calibration experiments. Traditionally, characterising materials to extract these key parameters requires setting up separate, often costly and time consuming laboratory experiments.The fourth contribution investigates the multiple energy benefits (MEBs) of energy efficiency (EE) measures implemented by small and medium-sized enterprises (SMEs) across five distinct European regions.The study uses a targeted survey questionnaire distributed across these five European regions to examine how local contextual factors influence organizational priorities regarding energy efficiency.The results reveal that having increased knowledge about the company energy use is the top overall MEB across the surveyed areas. However, individual regions show a distinct tendency to favour certain benefit categories over others even though all the investigated regions provide supportive energy policies; this suggests that policy availability is not the driving factor behind these observed disparities.The findings indicate that MEBs are socially constructed outcomes shaped by interactions between regional contexts and company practices. Therefore, the research emphasizes the need to analyse MEBs alongside EEMs rather than treating them as separate outcomes.When MEBs of energy efficiency are recognized not just a technical or economic calculation but a human-centric process shaped by social values, institutional structures and political priorities, this opens up a comprehensive understanding of how benefits emerge, are prioritized and valued across different contexts.The future of energy efficient technologies presents tremendous opportunities and is universally recognized as a cornerstone of global sustainability and decarbonization. The journey towards an energy-efficient future also present significant challenges which introduce several barriers. Policymakers worldwide must prioritize the integration of these technologies into existing frameworks, fostering innovation, and cultivating a lasting energy-conscious society.

• openalex https://doi.org/10.3389/fenef.2026.2002778first seen 2026-11-06 04:40:05