Search Results (85)

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article

The United Kingdom’s (UK) retrofit revolution is at a crossroads and the efficacy of retrofit interventions is not solely a function of insulation thickness. To truly slash emissions and lift households out of fuel poverty, we must solve the persistent problem of thermal bridging (TB), i.e., the hidden flaws that cause heat to escape, dampness to form, and well-intentioned retrofits to fail. This review moves beyond basic principles to spotlight the emerging tools and transformative strategies to make a difference. We explore the role of advanced modelling techniques, including finite element analysis (FEA), in pinpointing thermal and moisture-related risks, and how emerging materials like vacuum-insulated panels (VIPs) offer high-performance solutions in tight spaces. Crucially, we demonstrate how an integrated fabric-first approach, guided by standards like PAS 2035, is essential to manage moisture, ensure durability, and deliver the comfortable, low-energy homes the UK desperately needs. Therefore, achieving net-zero targets is critically dependent on the systematic upgrade of the building envelope, with the mitigation of TB representing a fundamental prerequisite. The EnerPHit approach applies a rigorous fabric-first methodology to eliminate TB and significantly reduce the building’s overall heat demand. This reduction enables the use of a compact heating system that can be efficiently powered by renewable energy sources, such as solar photovoltaic (PV). Moreover, this review employs a systematic literature synthesis to critically evaluate the integration of TB mitigation within the PAS 2035 framework, identifying key technical interdependencies and research gaps in whole-house retrofit methodology. This article provides a comprehensive review of established FEA modelling methodologies, rather than presenting results from original simulations. Full article

Care homes are an energy-intensive component of the health and social care sector, with high demands on heating, lighting, laundry, catering and medical technologies. This constant energy use makes care homes a notable contributor to global greenhouse gas emissions. Decarbonising care homes presents an opportunity to reduce emissions, operational costs, and deliver health co-benefits by improving air quality and thermal comfort. This scoping review mapped the international evidence on decarbonisation in care homes to inform sustainable practice and policy development. Guided by Joanna Briggs Institute methodology, seven databases (CINAHL, EMBASE, IEEE, MEDLINE, PubMed, Scopus, and Web of Science) were searched. Eligible studies included care home facilities, residents or staff with data managed in Covidence and extracted using the “The Greenhouse Gas Protocol Corporate Standard Inventory Accounting”. A total of 22 studies met the inclusion criteria. The evidence was concentrated around Scope 2 emissions, through efforts to monitor and reduce electricity use, while Scope 1 (facility emissions) and Scope 3 (supply chain emissions) remain comparatively underexplored. Evidence was fragmented and revealed risk aversion and care quality concerns related to adopting low-carbon technologies, as well as a growing interest in digital technologies and sustainable food procurement. Care homes should be prioritised within net zero healthcare frameworks, with targeted research, policy guidance, and investment to support decarbonisation. Full article

This paper presents a comprehensive methodology to forecast the daily energy demand associated with recharging private electric vehicles in urban areas. The approach is based on plausible scenarios regarding the penetration of battery-powered vehicles and the availability of charging infrastructure. Accurate space and time forecasting of charging activities and power requirements is a critical issue in supporting the transition from conventional to battery-powered vehicles for urban mobility. This technological shift represents a key milestone toward achieving the zero-emissions target set by the European Green Deal for 2050. The methodology leverages Floating Car Data (FCD) samples. The widespread use of On-Board Units (OBUs) in private vehicles for insurance purposes ensures the methodology’s applicability across diverse geographical contexts. In addition to FCD samples, the estimation of charging demand for private electric vehicles is informed by a large-scale, detailed survey conducted by ENEA in Italy in 2023. Funded by the Ministry of Environment and Energy Security as part of the National Research on the Electric System, the survey explored individual charging behaviors during daily urban trips and was designed to calibrate a discrete choice model. To date, the methodology has been applied to the Metropolitan Area of Rome, demonstrating robustness and reliability in its results on two different scenarios of analysis. Each demand/supply scenario has been evaluated in terms of the hourly distribution of peak charging power demand, at the level of individual urban zones or across broader areas. Results highlight the role of the different components of power demand (at home or at other destinations) in both scenarios. Charging at intermediate destinations exhibits a dual peak pattern—one in the early morning hours and another in the afternoon—whereas home-based charging shows a pronounced peak during evening return hours and a secondary peak in the early afternoon, corresponding to a decline in charging activity at other destinations. Power distributions, as expected, sensibly differ from one scenario to the other, conditional to different assumptions of private and public recharge availability and characteristics. Full article

The paper presents a linear optimisation model aimed at improving the design and operational efficiency of home energy systems (HESs). It focuses on integrating photovoltaic (PV) installations, hybrid heating systems, and emerging energy storage systems (ESSs). Driven by the EU climate policy and the evolution of the Polish electricity market, which have caused price volatility, the model examines the economic and technical feasibility of shifting detached and semi-detached houses towards low-emission or zero-emission energy self-sufficiency. The model simultaneously optimises the sizing and hourly operation of electricity and heat storage systems, using real-world data from PV output, electricity and gas consumption, and weather conditions. The key contributions include optimisation based on large data samples, evaluation of the synergy between a hybrid heating system with a gas boiler (GB) and a heat pump (HP), analysis of the impact of demand-side management (DSM), storage capacity decline, and comparison of commercial and emerging storage technologies such as lithium-ion batteries, redox flow batteries, and high-temperature thermal storage (HTS). Analysis of multiple scenarios based on three consecutive heating seasons and projected future conditions demonstrates that integrated PV and storage systems, when properly designed and optimally controlled, significantly lower energy costs for prosumers, enhance energy autonomy, and decrease CO₂ emissions. The results indicate that under current market conditions, Li-ion batteries and HTS provide the most economically viable storage options. Full article

Obstructive sleep apnea–hypopnea syndrome (OSAHS) is a prevalent sleep disorder strongly linked to increased cardiovascular and metabolic risk. While prior studies have explored snore-based analysis for OSAHS, they have largely focused on either detection or classification in isolation. Here, we present a two-stage framework that integrates precise snoring event detection with deep learning-based classification. In the first stage, we develop an Adaptive Multi-Feature Fusion Endpoint Detection algorithm (AMFF-ED), which leverages short-time energy, spectral entropy, zero-crossing rate, and spectral centroid to accurately isolate snore segments following spectral subtraction noise reduction. Through adaptive statistical thresholding, joint decision-making, and post-processing, our method achieves a segmentation accuracy of 96.4%. Building upon this, we construct a balanced dataset comprising 6830 normal and 6814 OSAHS-related snore samples, which are transformed into Mel spectrograms and input into ERBG-Net—a hybrid deep neural network combining ECA-enhanced ResNet18 with bidirectional GRUs. This architecture captures both spectral patterns and temporal dynamics of snoring sounds. The experimental results demonstrate a classification accuracy of 95.84% and an F1 score of 94.82% on the test set, highlighting the model’s robust performance and its potential as a foundation for automated, at-home OSAHS screening. Full article

Timber construction is increasingly promoted in Chile as a route to low-carbon, energy-efficient housing, yet public acceptance remains decisive for its diffusion. This study reports the first large-scale perception survey of timber buildings in Greater Concepción (N = 200) and contrasts key results with an earlier identical survey in Valdivia. Concepción residents strongly recognise timber’s thermal comfort attributes and associate wood housing with lower winter heating demand, a perception markedly stronger than in Valdivia. Conversely, 73% of Concepción respondents believe timber homes burn easily, but a majority also accept that modern engineering can mitigate this risk, indicating scope for targeted technical communication. Environmental perceptions are more ambivalent: although respondents value wood’s renewable origin, 42% doubt that timber construction reduces climate change, and many equate it with deforestation, echoing controversies around Chile’s plantation model. Cluster analysis reveals a techno-optimist subgroup coupling enthusiasm for energy savings with confidence in fire-safety innovations, suggesting a strategic constituency for demonstration projects. By situating end-user attitudes within national decarbonisation goals, this paper argues that region-specific outreach—emphasising verified energy-efficiency gains, certified sustainable forestry and visible fire-safety performance—can convert passive approval into active demand and accelerate Chile’s transition to a net-zero housing stock. Full article

This article demonstrates how positioning residents as active co-designers fundamentally transforms both the process and outcomes of carbon-positive building development. Through structured collaborative workshops, shared decision-making protocols, and continuous partnership throughout the building lifecycle, the REN+HOMES Positive Carbon Building methodology challenges the conventional expert-driven approach to sustainable construction. Developed and validated through the H2020 REN+HOMES project, this resident-centered approach achieved remarkable technical performance—65.9% reduction in final energy demand—while simultaneously enhancing community ownership and long-term sustainability practices. By integrating participatory design with Zero Emissions Building (ZEB) criteria, renewable energy systems, and national carbon offset programs, the methodology proves that resident collaboration is not merely beneficial but essential for creating buildings that truly serve both environmental and human needs. This research establishes a new paradigm where technical excellence emerges from authentic partnership between residents and sustainability experts, offering a replicable framework for community-driven environmental regeneration. Full article

This study provides a detailed dataset from two modern homes constructed inside an environmentally controlled chamber. These data are used to carefully calibrate a dynamic thermal simulation model of these homes. The calibrated models show good agreement with measurements taken under controlled conditions. The two case study homes, “The Future Home” and “eHome2”, were constructed within the University of Salford’s Energy House 2.0, and high-quality data were collected over eight days. The calibration process involved updating U-values, air permeability rates, and modelling refinements, such as roof ventilation, ground temperatures, and sub-floor void exchange rates, set as boundary conditions. Results demonstrated a high level of accuracy, with performance gaps in whole-house heat transfer coefficient reduced to 0.5% for “The Future Home” and 0.6% for “eHome2”, falling within aggregate heat loss test uncertainty ranges by a significant amount. The study highlights the improved accuracy of calibrated dynamic thermal simulation models, compared to results from the steady-state Standard Assessment Procedure model. By providing openly accessible calibrated models and a clearly defined methodology, this research presents valuable resources for future building performance modelling studies. The findings support the UK’s transition to dynamic modelling approaches proposed in the recently introduced Home Energy Model approach, contributing to improved prediction of energy efficiency and aligning with goals for zero carbon ready and sustainable housing development. Full article

Japan’s shift to a super-aged society is reshaping household carbon footprint (HCF) in ways that vary by age, income, and region. Drawing on a two-tier national–prefectural framework, we quantify the influence of demographic shifts on HCF and evaluate inequalities, and project prefectural HCF to 2050 under fixed 2005 technology and consumption baselines. Nationally, emissions follow an inverted-U age curve, peaking at the 50–54 s (2.16 tCO₂) and dropping at both the younger and older ends. Carbon inequality—the gap between high- and low-income households—displays the opposite U shape, being the widest below 30 and above 85. Regional HCF patterns add a further layer: while the inverted U persists, its peak shifts to the 60–64 s in high-income prefectures such as Tokyo—where senior emissions rise by 44% by 2050—and to the 45–49 s in low-income prefectures such as Akita, where younger age groups cut emissions by 58%. Although spatial carbon inequality narrows through midlife, it widens again in old age as eldercare and home energy needs grow. These findings suggest that a uniform mitigation trajectory overlooks key cohorts and regions. To meet the 2050 net-zero target, Japan should integrate age-, income-, and region-specific interventions—for example, targeted carbon pricing, green finance for middle-aged consumers, and less-urban low-carbon eldercare—into its decarbonization roadmap. Full article

Vehicle-to-home (V2H) technology enables electric vehicles (EVs) to supply power to residential loads, offering enhanced energy self-sufficiency and backup capabilities. Accurate voltage regulation is essential in such systems, especially under nonlinear and time-varying load conditions. The control method for three-phase four-wire (3P4W) converters plays a vital role in addressing these challenges. In the control configuration of such systems, the non-ideal proportional-resonant (PR) controller stands out due to its ability to reject periodic disturbances. However, the comprehensive study on the discretization of this controller for digital implementation in 3P4W systems has not been available in the literature to date. This paper presents a comparative study of several discretization methods for non-ideal PR controllers. The continuous-time complete transfer function of the integral term of non-ideal PR controllers is discretized using techniques such as Forward Euler, Backward Euler, Tustin, Zero-Order Hold, and Impulse Invariance. Additionally, the discretization methods based on two discrete integrators for the non-ideal PR controller, such as Forward Euler and Backward Euler, Backward Euler and Backward Euler plus computational delay, and Tustin and Tustin, are also evaluated. In the MATLAB/Simulink platform, through evaluating the performance of the non-ideal PR controllers, which are discretized using the above discretization methods, in controlling the output voltage of the 3P4W converter in the V2H application under nonlinear load scenarios, including substantial and sudden changes in load, the discretization method Backward Euler and Backward Euler plus delay is recommended. Full article

Motors, as the core carriers of pollution-free power, realize efficient electric energy conversion in clean energy systems such as electric vehicles and wind power generation, and are widely used in industrial automation, smart home appliances, and rail transit fields with their low-noise and zero-emission operating characteristics, significantly reducing the dependence on fossil energy. As the requirements of various application scenarios become increasingly complex, it becomes particularly important to accurately and quickly design the sealing structure of motors. However, traditional design methods show many limitations when facing such challenges. To solve this problem, this paper proposes hybrid models of machine learning that contain polynomial regression and optimization XGBOOST models to rapidly and accurately predict the sealing performance of motors. Then, the hybrid model is combined with the simulated annealing algorithm and multi-objective particle swarm optimization algorithm for optimization. The reliability of the results is verified by the mutual verification of the results of the simulated annealing algorithm and the particle swarm optimization algorithm. The prediction accuracy of the hybrid model for data outside the training set is within 2.881%. Regarding the prediction speed of this model, the computing time of ML is less than 1 s, while the computing time of FEA is approximately 9 h, with an efficiency improvement of 32,400 times. Through the cross-validation of single-objective optimization and multi-objective optimization algorithms, the optimal design scheme is a groove depth of 0.8–0.85 mm and a pre-tightening force of 80 N. The new method proposed in this paper solves the limitations in the design of motor sealing structures, and this method can be extended to other fields for application. Full article

The residential sector accounts for over a third of the world’s energy use. Even though this ratio is lower in Mexico, there is a pressing housing deficit, especially regarding low-cost homes. This research aimed to create a reference building (RB) to understand the current energy consumption of multi-family buildings across different climatic zones. The aim was to assess their energy performance and promote reduced energy requirements as a guideline for designing and constructing affordable, low-energy, or zero-energy buildings. The present work conducts a diagnosis of the current energy consumption of multi-family buildings in eight cities in Mexico. First, a reference building was developed to represent typical Mexican building geometry and construction practices, and then the building’s fixed and variable energy requirements were simulated. Finally, a comparison was made between the energy requirement and the data reported by the national energy survey. Therefore, it was possible to generate a reference building from national data sources complying with national regulations, where materials, occupant behavior, and equipment were chosen to help represent the building’s thermal behavior. Domestic water heating was identified as a driver of variable energy requirements in all cities. In contrast, the simulated heating and cooling requirements were directly linked to the city’s climate. Electricity bills tended to mostly correspond with the results that excluded the use of heating systems. Lastly, while comparing LPG (Liquified Petroleum Gas) consumption was challenging due to the unavailability of national data, LPG requirements were closely estimated for temperate cities. The definition of a reference building is an important step towards developing nZEB in Mexico, as these buildings are valuable tools that can contribute to the energy evaluation of specific types of buildings. This characteristic makes them convenient for revising a building code or setting new national energy policy goals. Full article

The compound energy system is an important component of zero-carbon buildings. Due to the complex form of the system and the difficult-to-capture characteristics of thermo-electric coupling interactions, the operation control of the zero-carbon building’s energy system is difficult in practical engineering. Therefore, it is necessary to carry out relevant optimization methods. This paper investigated the current research status of the control and scheduling of compound energy systems in zero-carbon buildings at home and abroad, selected a typical zero-carbon building as the research object, analyzed its energy system’s operational data, and proposed an operation scheduling algorithm based on day-ahead flexible programming and intraday rolling optimization. The multi-energy flow control algorithm model was developed to optimize the operation strategy of heat pump, photovoltaic, and energy storage systems. Then, the paper applied the algorithm model to a typical zero-carbon building project, and verified the actual effect of the method through the actual operational data. After applying the method in this paper, the self-absorption rate of photovoltaic power generation in the building increased by 7.13%. The research results provide a theoretical model and data support for the operation control of the zero-carbon building’s compound energy system, and could promote the market application of the compound energy system. Full article

Energy performance improvements in existing homes play a substantial role in the achievement of the UK’s net-zero emissions target. However, retrofitting dwellings remains a particularly challenging task in the UK, where traditional dwellings make up a large part of the building repository. Traditional dwellings’ contribution to decarbonization has not yet been fully realized due to the risks imposed to the thermo-hygrometric balance of their constructions and to their heritage value. These tend to hinder the “fabric-first” approach for the retrofit of such dwellings, where active measures are often prioritized. The aim of this research is to propose a systemic approach to intervene in Traditional Listed Dwellings (TLDs) to improve their energy performance by means of passive retrofit measures and to shape a more future-proof heritage. A mixed methodology was developed that utilizes 19th C TLD case studies (CSs) in South-East England and dynamic energy simulation (DES) to investigate their current energy performance and possible improvements using responsible, safe and effective energy retrofit scenarios. Providing an overview of the methodology adopted in this research, this paper presents the main results of this study. This paper highlights the savings associated with the best-performing combinations of retrofit measures and the areas of intervention where the highest energy and carbon savings can be achieved. Full article