Search Results (120)

Long-term energy sustainability in the built environment depends not only on deploying renewables but also on maintaining high energy efficiency that consistently lowers demand and enables more effective use of low-carbon electricity over time. This paper presents an illustrative case study that demonstrates a low-data, EPC/audit-based screening workflow for assessing operational energy, carbon, and water-related indicators in a non-residential building. An explanatory case study is conducted for a mixed-use logistics facility in Poland (≈610 m²), combining approaches to useful/final/primary energy indicators with operational carbon and water footprints. The operational water footprint is evaluated as a screening metric (L/kWh) applied to the annual electricity balance and tested across PV self-consumption levels (25/50/75%) to reflect the role of energy management and flexibility. The results indicate that an efficiency-oriented modernization pathway supported by PV integration (≈64 kWp; ~57,350 kWh/yr) reduces the primary energy performance indicator EP from 154 to 62.5 kWh/m²·yr, corresponding to a 59% reduction in annual primary energy demand. The operational water footprint indicator decreases nearly linearly with increasing PV self-consumption, demonstrating that long-term benefits depend on sustained efficiency and on maximizing on-site renewable utilization through controls, demand shifting, and/or storage. Overall, the framework supports transparent benchmarking and the development of staged pathways for integrating renewable and low-carbon energy systems into logistics-building portfolios, while maintaining an analytical focus on operational energy, carbon, and water performances. Full article

Background and Objectives: Obesity affects over 1.9 billion adults globally, with a disproportionately higher prevalence in Saudi Arabia among women. While excessive adiposity is known to impair respiratory mechanics and lung function, its relationship with respiratory muscle strength and exercise energy expenditure remains inadequately elucidated. This study examined differences in respiratory muscle strength, metabolic equivalents (METs) of physical activity, and energy expenditure during exercise between adults with normal and high body fat percentage (BF%) and explored the statistical role of body fat as a potential mediator in the cross-sectional association between respiratory muscle strength and energy expenditure. Methods: In this cross-sectional study, 126 Saudi women aged 18–45 years (mean age: 21.7 ± 4.2 years) were stratified into normal (n = 63) and high (n = 63) BF% groups. Body composition was assessed via bioelectrical impedance analysis, and respiratory muscle strength (MIP and MEP) was measured using a MicroRPM device. Peak oxygen consumption (VO_2peak) and energy expenditure were obtained through the Bruce Submaximal Treadmill Protocol, and physical activity was self-reported via the IPAQ. Hierarchical regression and structural equation modeling were used to examine variable associations and explore statistical mediation patterns. Results: Participants with high body fat demonstrated significantly low MIP (−26%) and MEP (−31%), low VO_2peak (−13%), and approximately 26% high energy expenditure during exercise compared to the normal-BF group (all p < 0.001), despite comparable self-reported physical activity levels. Body fat percentage was the most strongly associated with energy expenditure (β = 0.078, R² = 0.329), with maximal inspiratory pressure contributing an additional 7.3% of explained variance in hierarchical regression (total R² = 0.414). Mediation analyses revealed that body fat percentage was statistically consistent with a partial mediation model in the relationship between MIP and energy expenditure (indirect association = −0.016, p = 0.033), accounting for 27% of the total association, and between MEP and energy expenditure (indirect association = −0.013, p = 0.035), accounting for 38% of the total association. Conclusions: High BF% is independently associated with low respiratory muscle strength and high exercise metabolic cost. Body fat is statistically associated with (and consistent with a mediating role in) an inverse relationship between respiratory muscle strength and energy expenditure. Alternative directional relationships and shared underlying factors may explain these observations. Full article

The inherent bottleneck of the von Neumann architecture and the limited power budget of edge devices necessitate energy-efficient hardware solutions for artificial intelligence. Memristor-based In-Memory Computing (IMC) has emerged as a promising candidate; however, the high-power consumption of peripheral circuits, particularly Analog-to-Digital Converters (ADCs), and the reliability issues of memristive devices remain significant challenges. In this paper, we propose a hybrid Convolutional Spiking Neural Network (CSNN) architecture designed for resource-constrained edge computing. Our approach integrates digital Non-Leaky Integrate-and-Fire (NLIF) neurons with Knowm Self-Directed Channel (SDC) memristor-based synapses in a 1T1R crossbar array. To maximize power efficiency, we replace conventional high-resolution ADCs with a streamlined readout circuit utilizing a Current Sense Amplifier (CSA) and a 1-bit comparator. Furthermore, we employ an intensity-to-latency temporal coding scheme to minimize spike activity and mitigate device endurance degradation. We validated the proposed system using the MNIST dataset, achieving a classification accuracy of 97.8%, which is comparable to state-of-the-art floating-point SNNs using supervised learning methods. Power analysis confirms that our 1-bit readout method consumes only 18.4% of the energy required by an 8-bit ADC-based approach while maintaining negligible accuracy loss. Additionally, the deterministic single-spike nature of our temporal coding significantly reduces write stress on memristors compared to rate coding. These results demonstrate that the proposed hybrid CSNN offers a robust and energy-efficient solution for neuromorphic edge intelligence. Full article

This paper presents a case study of a Home Energy Management System (HEMS) integrating photovoltaic (PV) generation, battery energy storage (BES), thermal storage, and a heat pump in a single-family household operating under a dynamic electricity tariff. The analysis is based on real operational data and focuses on system performance under varying solar generation conditions. The results show that during sunny days, the battery storage absorbs the entire surplus PV generation until reaching full capacity, i.e., 10 kWh, effectively preventing curtailment and maximizing self-consumption. On days with limited solar production, the system actively utilizes the available storage capacity by shifting energy use in time and, when economically justified, temporarily charging the battery from the grid during low-price periods. This strategy reduces electricity purchases during peak-price hours and stabilizes household energy costs. For the analyzed case, daily PV generation self-consumption exceeded 70% on high-generation days, while the application of storage-based load shifting under dynamic tariffs reduced daily electricity costs by up to 30% compared to a fixed-rate tariff. The study confirms that the economic and operational performance of residential energy systems under dynamic pricing depends primarily on adaptive storage control rather than on PV capacity alone, highlighting the central role of battery energy storage in year-round energy optimization. Full article

Intelligent energy management systems are increasingly necessary for integrating renewable energy sources within microgrids. This paper investigates the application of a reinforcement learning (RL) neural network to optimize the operation of an electrochemical storage system in an environment composed of residential loads, commercial loads, and a photovoltaic plant, all connected to the grid. A dataset combining market purchase prices, photovoltaic generation, and residential and commercial load profiles was generated and used to train a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent with the primary goal of deriving a reliable and adaptive post-training policy capable of maximizing photovoltaic self-consumption, minimizing operational costs through intelligent price arbitrage, and ensuring strict compliance with battery physical constraints. The system state includes battery state of charge, load demand, PV generation, and normalized market purchase prices, whereas the action represents the battery’s charge/discharge power, which is restricted from exporting energy to the grid. Results show that the agent learns to effectively store surplus PV energy and minimize grid dependency through dynamic charge management. The proposed approach outperforms strategies based solely on storing surplus self-generated energy and maintains the battery within safe operational limits. Tests with previously unseen data demonstrate robust, adaptive, and economically efficient energy management, highlighting the potential of reinforcement learning in intelligent energy systems. Full article

With the increasing popularity of photovoltaic (PV) equipment in residential and commercial buildings, there is a pressing need for systems that maximize energy efficiency and self-consumption. This paper introduces an integrated management framework for retrofitting existing infrastructures, enabling high photovoltaic (PV) self-consumption in residential buildings through a rule-based control strategy. The framework supports three service classes—defined by user-level Quality of Service (QoS) parameters—and monitors battery voltage along with grid power exchange to coordinate heat pumps, batteries, and hot water cylinders. Experimental deployment in a residential testbed achieved up to 89% PV self-consumption while keeping daily grid usage below 0.5 kWh. Ablation experiments on battery size further demonstrated the approach’s robustness to reduced storage capacities. The use of Commercial-Off-The-Shelf (COTS) components underscores the minimal intrusiveness of this solution, highlighting its potential for seamlessly integrating diverse, vendor-specific equipment into a coordinated control system. Full article

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article

Smart microgrids are localized energy systems that integrate distributed energy resources, such as photovoltaics (PVs) and battery storage, to optimize energy use, enhance reliability, and minimize environmental impacts. This paper investigates the operation of a smart microgrid installed at the Hellenic Mediterranean University (HMU) campus in Heraklion, Crete, Greece. The system, consisting of PVs and battery storage, operates under a zero feed-in scheme, which maximizes on-site self-consumption while preventing electricity exports to the main grid. With increasing PV penetration and growing grid congestion, this scheme is an increasingly relevant strategy for microgrid operations, including university campuses. A properly sized PV–battery microgrid operating under zero feed-in operation can remain financially viable over its lifetime, while additionally it can achieve significant environmental benefits. The study performed at the HMU Campus utilizes measured hourly data of load demand, solar irradiance, and ambient temperature, while PV and battery components were modeled based on real technical specifications. The study evaluates the system using financial and environmental performance metrics, specifically net present value (NPV) and annual greenhouse gas (GHG) emission reductions, complemented by sensitivity analyses for battery technology (lead–carbon and lithium-ion), load demand levels, varying electricity prices, and projected reductions in lithium-ion battery costs over the coming years. The findings indicate that the microgrid can substantially reduce grid electricity consumption, achieving annual GHG emission reductions exceeding 600 tons of CO₂. From a financial perspective, the optimal configuration consisting of a 760 kWp PV array paired with a 1250 kWh lead–carbon battery system provides a system autonomy of 46% and achieves an NPV of EUR 1.41 million over a 25-year horizon. Higher load demands and electricity prices increase the NPV of the optimal system, whereas lower load demands enhance the system’s autonomy. The anticipated reduction in lithium-ion battery costs over the next 5–10 years is expected to provide improved financial results compared to the base-case scenario. These results highlight the techno-economic viability of zero feed-in microgrids and provide valuable insights for the planning and deployment of similar systems in regions with increasing renewable penetration and grid constraints. Full article

Electric vehicles (EVs) and renewable energy generation are widely regarded as key drivers of low-carbon transformation in the transportation and energy sectors due to their emission reduction potential and environmental benefits. However, the inherent intermittency and volatility of photovoltaic (PV) power, coupled with increasingly stochastic and disorderly EV charging demand, pose significant challenges to grid stability and local renewable energy utilization. To address these issues, this paper proposes a dynamic pricing optimization approach based on a Stackelberg game framework, in which the PV charging station operator acts as the leader and EV users as followers. Unlike conventional models, the proposed framework explicitly incorporates user psychological expectations and response deviations through a three-stage “dead-zone-linear-saturation” responsiveness structure, thereby capturing the uncertainty and partial rationality of EV charging behavior. The upper-level objective seeks to maximize operator profit and enhance PV self-consumption, while the lower-level objective minimizes user energy cost under price-responsive charging decisions. The bilevel optimization problem is solved via a differential evolution (DE) algorithm combined with YALMIP + CPLEX. Simulation results for a regional PV-EV charging station show that the proposed strategy increases PV self-consumption to about 90.5% and shifts the load peak from 18:00–20:00 to 10:00–15:00, effectively aligning charging demand with PV output. Compared with both flat and standard time-of-use (TOU) tariffs, the dynamic pricing scheme yields higher operator profit (about 7% improvement over flat pricing) while keeping total user energy expenditure essentially unchanged. In addition, the cumulative carbon reduction cost over the operating cycle is reduced by approximately 4.1% relative to flat pricing and 1.9% relative to TOU pricing, demonstrating simultaneous economic and environmental benefits of the proposed game-based dynamic pricing framework. Full article

This paper presents the design, modeling and experimental validation of an on-grid photovoltaic system with self-consumption, sized for the sustainable supply of a water pumping station. The system, composed of 68 photovoltaic panels, uses an architecture based on a Boost DC-DC converter controlled by the Perturb and Observe algorithm, raising the operating voltage to a high-voltage DC bus to maximize the conversion efficiency. The study integrates dynamic performance analysis through simulations in the Simulink environment, testing the stability of the DC bus under sudden irradiance shocks, with rigorous experimental validation based on field production data. The simulation results, which indicate a peak DC power of approximately 34 kW, are confirmed by real monitoring data that records a maximum of 35 kW, the error being justified by the high efficiency of the panels and system losses. Long-term validation, carried out over three years of operation (2023–2025), demonstrates the reliability of the technical solution, with the system generating a total of 124.68 MWh. The analysis of energy flows highlights a degree of self-consumption of 60.08%, while the absence of chemical storage is compensated for by injecting the surplus of 49.78 MWh into the national grid, which is used as an energy buffer. The paper demonstrates that using the grid to balance night-time or meteorological deficits, in combination with a stabilized DC bus, represents an optimal technical-economic solution for critical pumping infrastructures, eliminating the maintenance costs of the accumulators and ensuring continuous operation. Full article

The integration of smart electricity management models in buildings is a key strategy for improving living comfort and optimizing energy efficiency. The incentive mechanisms introduced by the Italian regulatory framework for widespread self-consumption and energy communities encourage the deployment of smart management systems within Collective Self-Consumption Groups (CSGs) and Renewable Energy Communities (RECs). These mechanisms drive the search for solutions that combine occupant well-being with economic benefits, thereby fostering citizen participation in aggregation models that play a key role in the transition towards a progressively decarbonized electricity system. In this context, an optimization model for the management of residential heat pumps is proposed, aimed at identifying the best compromise between thermal comfort, electricity consumption, and economic benefits. The approach developed in the research encourages citizens to take an active role without the need for burdensome commitments and/or significant changes in their daily habits, in line with the importance that users themselves attribute to these aspects. To demonstrate the potential of the proposed approach, a case study was developed on a residential building located in Sardinia (Italy). The implementation of an optimization model aimed at simultaneously maximizing economic benefits and indoor thermal comfort is simulated. The model’s economic and energy performance is assessed and compared with the results obtained using different advanced heat pump control and management strategies. Full article

This study optimized a homogeneous photo-Fenton process for the simultaneous degradation of the emerging pesticides atrazine and methomyl in water using Response Surface Methodology (RSM). A synthetic agricultural effluent containing 2.0 mg L⁻¹ of each pesticide (COD = 103.2 mg O₂ L⁻¹; TOC = 26.1 mg C L⁻¹; BOD₅ = 45.8 mg O₂ L⁻¹) was treated in a recirculating UV–H₂O₂/Fe²⁺ reactor. A 2³ factorial design with replication and five central points identified the H₂O₂/Fe²⁺ ratio and irradiation time as the main factors controlling mineralization, achieving up to 88.9% COD removal in the best screening run. Steepest-ascent experiments were then performed to approach the region of maximum response, followed by a rotatable Central Composite Design (20 runs). The resulting quadratic model explained 98.14% of the COD variance (R² = 0.9814; adjusted R² = 0.9646; predicted R² = 0.8591; CV = 0.2736%) and predicted a maximum COD removal of 94.5% at a volumetric flow rate of 0.466 L min⁻¹, a Fenton ratio of 12.713 mg mg⁻¹, and a treatment time of 71.0 min. Experimental validation under these optimized conditions yielded highly reproducible removals of 94.2 ± 0.04% COD and 81% TOC, confirming the predictive capability of the RSM model and demonstrating a high degree of organic mineralization. The response surfaces revealed that increasing the Fenton ratio enhances oxidation up to an optimum, beyond which hydroxyl-radical self-scavenging slightly decreases efficiency. Overall, the integration of multivariable experimental design and RSM provided a robust framework to maximize photo-Fenton performance with moderate reagent consumption and operating time, consolidating this process as a viable alternative for the mitigation of pesticide-laden agricultural wastewaters. Full article

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

It is well known that the features of self-driving vehicles depend on communication technologies to demonstrate their benefits. Since these technologies are still under development and face numerous obstacles, this highlights the need to develop a modern approach to solving congestion during the transitional phase. In this study, we worked on developing an integrated new operational strategy that maximizes the benefits of the variable speed limit strategy and expands its impact by coordinating its operation with the spacing policy mechanism in vehicles equipped with adaptive cruise control (ACC) to provide an innovative approach aims to operate vehicles with low levels of autonomy and leverage their ability to maintain short time gaps to operate as an effective category to improve traffic conditions, aided by existing transportation systems. To achieve this, we employed PTV-VISSIM to develop the VSL algorithm, which was coded using the VisVAP interface. We also used VISSIM features to model and develop the characteristics of the ACC vehicles and the spacing policy. Different control strategies were tested individually and in combination at various penetration rates, and the results demonstrated the superiority of our proposal to integrate the VSL mechanism with the short-time gap recommendation strategy. The strategy’s effect was also evident in emissions reductions of 52% to 86% and in fuel consumption decreases of 52% to 87% compared to the no-control scenario, and of 56% to 28% compared to the typical VSL scenario, supporting an environmental sustainability approach in traffic strategies. Full article

Objectives: The present study analyzes the effects of a high-intensity interval training (HIIT) program based on the Tabata method on physiological and psychological variables in contemporary dancers (n = 10) and sedentary individuals (n = 8), who performed a 10-week protocol, with sessions of self-loading exercises structured in intervals of 20 s of effort and 10 s of rest three times a week. Methods: Parameters of body composition, muscle strength, aerobic and anaerobic capacity, heart rate variability, as well as perceptions of health, anxiety, stress, sleep quality, and levels of physical activity and sedentary lifestyle were evaluated. Results: The results showed that no significant changes occurred in most body composition variables, except for visceral fat, where group differences were observed (F = 5.66, p = 0.030, η²ₚ = 0.261). In the indicators of strength and power, the dancers improved the height and relative power of the jump (F = 5.996, p = 0.026, η²ₚ = 0.273), while the sedentary ones increased the strength of the handgrip (p = 0.023). In terms of functional performance, both groups significantly increased anaerobic endurance (F = 10.374, p = 0.005, η²ₚ = 0.393), although no changes were recorded in maximal oxygen consumption or heart rate variability (p > 0.05). On a psychological level, improvements in healthy lifestyle habits and a decrease in the trait anxiety variable were evidenced in dancers (p = 0.023), while in sedentary participants no relevant effects were found. Conclusions: In conclusion, the Tabata protocol may represent an efficient and complementary strategy to enhance strength, anaerobic power, and psychological well-being, particularly among dancers. The observed improvements suggest potential benefits related to movement quality, injury prevention, and general physical conditioning. Full article