Search Results (1,171)

Hybrid geothermal–solar systems leverage complementary resources to enhance efficiency, dispatchability, and low-carbon supply. This review compares mainstream configurations (solar-preheating configurations, solar-superheating configuration, and other emerging concepts) and reports typical performance gains—thermal efficiency of 5–80% and exergy efficiency up to ~60%—observed across resource contexts. Findings indicate that preheating routes are generally preferable under medium direct normal irradiance (DNI) and operation-and-maintenance (O&M)-constrained conditions, while superheating routes become attractive at high DNI with thermal storage; integrated multigeneration systems can deliver system-level benefits for multi-energy parks and district applications. In addition, this paper identifies technical bottlenecks—source matching, storage dependence, and the absence of a unified evaluation—and summarizes control/optimization strategies, including emerging advanced artificial-intelligence algorithms. In addition, the review highlights a standardized comprehensive performance evaluation framework, which covers thermal and exergy efficiency, net power output, complexity, the levelized cost of electricity (LCOE), reliability, and storage. Finally, according to the research status and findings, future research directions are proposed, which pave the way for more effective exploitation of geothermal and solar energy. Full article

Accurate cooling load forecasting in chiller units is critical for building energy optimization, yet remains challenging due to non-stationary nonlinear dynamics driven by coupled external weather variability (solar radiation, ambient temperature) and internal thermal loads. Conventional models fail to capture the spatiotemporal coupling inherent in load time series, violating their stationarity assumptions. To address this, this research proposes OptiNet, a spatiotemporal forecasting framework integrating patch-specific dynamic filtering with graph neural networks. OptiNet partitions multi-sensor data into non-overlapping time patches to develop a dynamic spatiotemporal graph. A learnable routing mechanism then performs adaptive dependency filtering to capture time-varying temporal–spatial correlations, followed by graph convolution for load prediction. Validated on long-term industrial logs (52,075 multi-sensor samples at 20 min; district cooling plant in Zhangjiang, Shanghai, with multiple chillers, towers, pumps, building meters, and a weather station), OptiNet achieves consistently lower MAE and MSE than Graph WaveNet across 6–144-step horizons and sampling frequencies of 20–60 min; among 30 set-tings it leads in 26, with MSE reductions up to 27.8% (60 min, 72-step) and typical long-horizon (72–144 steps) gains of ≈2–18% MSE and ≈1–15% MAE. Crucially, the model provides interpretable spatial-temporal dependencies (e.g., “Zone B solar radiation influences Unit 2 load with 4-h lag”), enabling data-driven chiller sequencing strategies that reduce electricity consumption by 12.7% in real-world deployments—directly advancing energy-efficient building operations. Full article

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

This study investigates the effect of rotation step size on the performance of flat-plate solar collectors (FPSC) equipped with single-axis tracking. Numerical simulations were carried out in EnergyPlus, coupled with a custom Python interface enabling dynamic control of collector orientation. The analysis was carried out for the city of Kragujevac in Serbia, located in a temperate continental climate zone, based on five representative summer days (3 July–29 September) to account for seasonal variability. Three collector types with different efficiency parameters were considered, and inlet water temperatures of 20 °C, 30 °C, and 40 °C were applied to represent typical operating conditions. The results show that single-axis tracking increased the incident irradiance by up to 28% and the useful seasonal heat gain by up to 25% compared to the fixed configuration. Continuous tracking (ψ = 1°) achieved the highest energy yield but required 181 daily movements, which makes it mechanically demanding. Stepwise tracking with ψ = 10–15° retained more than 90–95% of the energy benefit of continuous tracking while reducing the number of daily movements to 13–19. For larger steps (ψ = 45–90°), the advantage of tracking decreased sharply, with thermal output only 5–10% higher than the fixed case. Increasing the inlet temperature from 20 °C to 40 °C reduced seasonal heat gain by approximately 30% across all scenarios. Overall, the findings indicate that relative single-axis tracking with ψ between 10° and 15° provides the most practical balance between energy efficiency, reliability, and economic viability, making it well-suited for residential-scale solar thermal systems. This is the first study to quantify how discrete rotation steps in single-axis tracking affect both thermal and economic performance of flat-plate collectors. The proposed EnergyPlus–Python model demonstrates that a 10–15° step offers 90–95% of the continuous-tracking energy gain while reducing actuator motion by ~85%. The results provide practical guidance for optimizing low-cost solar-thermal tracking in continental climates. Full article

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R² ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization. Full article

Localized employment gains from new or expanded fossil fuel development commonly are cited by its proponents in response to sustainability-related concerns raised by local drilling area residents. This paper analyzes local employment effects in drilling areas within the Marcellus shale formation in the state of Pennsylvania, USA. The Marcellus shale formation was one of the early natural gas fracking boom development areas globally, so these local employment outcomes can inform future policy decisions on not-yet-developed shale gas formations worldwide. As long-term sustainable jobs are a key part of any locale’s sustainable development program, the magnitude and persistence of employment gains in the local drilling area is highly relevant. The existing research literature on employment effects from increased shale gas extraction is dominated by usage of panel estimation on annual data at the U.S. state/county level. The innovative contribution of this paper is its use of monthly data, sub-state local areas (67 counties within PA), and a parsimonious vector autoregression model (VAR) estimated separately for each of the 67 counties. The estimated VAR models are used to ascertain whether Marcellus shale drilling activity in PA led to actual county-level employment above forecasted based on data prior to the shale boom. Actual versus forecasted employment is compared from 2010–2019. Higher than forecasted employment findings were much more likely to occur in approximately the top quarter of drilling counties, with the observed gains being modest. Most importantly, however, any employment gains above forecast were short-lived, gone within four years in most counties. Given the modest and temporary local employment gains found and the many known potential damages to local residents and the environment from intensive drilling, it is questionable that the local areas in the Marcellus shale formation most intensively drilled benefited overall from the shale gas extraction. These findings are germane to ongoing current debates about expanding natural-gas-fired electricity generation, versus solar plus storage, to meet anticipated large rises in electricity demand from rapid data center development globally. Full article

Hybrid PV-TEG systems can harvest both solar electrical and thermoelectric power, but their operating point drifts with irradiance, temperature gradients, partial shading, and load changes—often yielding multi-peak P-V characteristics. Conventional MPPT (e.g., P&O) and fixed-structure integer-order PID struggle to remain fast, stable, and globally optimal in these conditions. To address fast, robust tracking in these conditions, we propose an adaptive fractional-order PID (FOPID) MPPT whose parameters (K_p, K_i, K_d, λ, μ) are auto-tuned by the red-billed blue magpie optimizer (RBBMO). RBBMO is used offline to set the controller’s search ranges and weighting; the adaptive law then refines the gains online from the measured ΔV, ΔI slope error to maximize the hybrid PV-TEG output. The method is validated in MATLAB R2024b/Simulink 2024b, on a boost-converter–interfaced PV-TEG using five testbeds: (i) start-up/search, (ii) stepwise irradiance, (iii) partial shading with multiple local peaks, (iv) load steps, and (v) field-measured irradiance/temperature from Shanxi Province for spring/summer/autumn/winter. Compared with AOS, PSO, MFO, SSA, GHO, RSA, AOA, and P&O, the proposed tracker is consistently the fastest and most energy-efficient: 0.06 s to reach 95% MPP and 0.12 s settling at start-up with 1950 W·s harvested (vs. 1910 W·s AOS, 1880 W·s PSO, 200 W·s P&O). Under stepwise irradiance, it delivers 0.95–0.98 kJ at t = 1 s and under partial shading, 1.95–2.00 kJ, both with ±1% steady ripple. Daily field energies reach 0.88 × 10⁻³, 2.95 × 10⁻³, 2.90 × 10⁻³, 1.55 × 10⁻³ kWh in spring–winter, outperforming the best baselines by 3–10% and P&O by 20–30%. Robustness tests show only 2.74% power derating across 0–40 °C and low variability (Δv^max typically ≤ 1–1.5%), confirming rapid, low-ripple tracking with superior energy yield. Finally, the RBBMO-tuned adaptive FOPID offers a superior efficiency–stability trade-off and robust GMPP tracking across all five cases, with modest computational overhead. Full article

As manufacturing industries pursue net-zero emission (NZE) goals, hybrid manufacturing processes that integrate additive manufacturing (AM) with traditional casting techniques are gaining traction for their sustainability potential across the globe. Therefore, this work presents a “gate-to-gate” life cycle assessment (LCA) comparing AM-assisted sand casting (AM-SC) and AM-assisted investment casting (AM-IC), for Al-Si5-Cu3 alloy as a case material, under various energy scenarios including a conventional grid mix and renewable sources (wind, solar, hydro, and biomass). This study compares multiple environmental impact categories based on the CML 2001 methodology. The outcomes show that AM-SC consistently outperforms AM-IC in most impact categories. Under the grid mix scenario, AM-SC achieves 31.57% lower GWP, 19.28% lower AP, and 21.15% lower EP compared to AM-IC. AM-SC exhibits a 90.5% reduction in “Terrestrial Ecotoxicity Potential” and 75.73% in “Marine Ecotoxicity Potential”. Wind energy delivers the most significant emission reduction across both processes, reducing GWP by up to 98.3%, while AM-IC performs slightly better in HTP. These outcomes of the study offer site-specific empirical insights that support strategic decision-making for process selection and energy optimisation in casting. By quantifying environmental trade-offs aligned with India’s current energy mix and future renewable targets, the study provides a practical benchmark for tracking incremental gains toward the NZE goal. This work followed international standards (ISO 14040 and 14044), and the data were validated with both foundry records and field measurements; this study ensures reliable methods. The findings provide practical applications for making sustainable choices in the manufacturing process and show that the AM-assisted conventional manufacturing process is a promising route toward net-zero goals. Full article

Kinetic façades represent a climate-responsive design solution that improves building adaptability by responding to seasonal needs such as daylighting and shading. They offer an attractive retrofit strategy that improves both the esthetics and environmental performance of buildings. This study investigated the integration of an origami-inspired kinetic façade into a student dormitory building located in Dhahran, Saudi Arabia. Using numerical simulations, 35 façade configurations were analyzed under varying conditions of façade orientations, closure ratios (from 5% to 95%), and cavity depths (from 20 cm to 100 cm). The findings highlight the critical impact of kinetic façade design characteristics on daylight availability and solar exposure and the required trade-off between these two variables. In this context, this study observed that at higher façade closure ratios, increasing cavity depth could effectively mitigate daylight reduction by promoting reflected daylight penetration inside the cavity. As for heat gains and cooling load reduction, mid-range façade closure, 50 cm in this study, achieved balanced performance across the three examined orientations. However, the southern façade showed slightly higher efficiency compared to the eastern and western façades, which achieved lower cooling reductions and showed a similar UDI compromise. Thus, a dynamic façade operation is recommended, where higher closure ratios could be applied during peak solar hours on the east in the morning and the west in the afternoon to maximize cooling savings, while moderate closure ratios can be maintained on the south to preserve daylight. Future work should incorporate real-time climatic data and smart control technologies to further optimize kinetic façade performance. Full article

Photocatalytic pathogen inactivation is gaining increasing importance due to the rising number of microbial species resistant to conventional antibacterial agents. Titanium dioxide (TiO₂)-based photocatalysts have emerged as a promising solution, being not only potent antibacterial agents but also environmentally friendly and capable of simultaneously degrading organic pollutants. This review summarizes recent advances in the antibacterial performance of different TiO₂ modifications, including commercial nanopowders, nanoparticles with various morphologies, thin films, composites, and polymer-supported nanostructures, all primarily activated under UV light. Given the limited ability of pristine TiO₂ to harvest solar radiation, we also highlight the most recent strategies for designing visible-light-responsive TiO₂, such as doping, incorporation of plasmonic metal nanoparticles, formation of heterostructures, and interfacial charge transfer complexes. In addition, we discuss the fundamental structural features of TiO₂, the mechanisms of reactive oxygen species (ROS) generation involved in bacterial inactivation, and kinetic models describing antibacterial efficiency. These insights aim to advance the understanding and development of eco-friendly, cost-effective, and sustainable photocatalytic disinfection technologies. Full article

Solar photovoltaic systems now produce the lowest-cost electricity in history and coupling with agriculture in agrivoltaics increases crop yields. This indicates solar will continue to experience explosive growth. Concerns exist, however, about the long-term end-of-life decommissioning of solar farms. For example, due to fossil fuel decommissioning mismanagement, Alberta is inundated with orphaned oil and gas wells that have remediation cost estimates of CAD$100 billion. Such comparisons have prompted preemptive legislation targeting solar farms, but is the fear justified? This study addresses this question by (1) analyzing warranted and actual lifespans of key agrivoltaic system components, (2) experimentally measuring microclimate impacts of two agrivoltaic arrays (fully powered with electricity extraction and unpowered to simulate post-inverter-failure conditions) and (3) quantifying agrivoltaic yield gains based on crops previously shown to respond positively to such conditions. Experimental results indicate that unpowered photovoltaic shading not only moderates soil temperatures but also enhances soil moisture conservation relative to unshaded conditions. This study demonstrates that agrivoltaic systems, even after the cessation of power generation, can continue to deliver meaningful agronomic and economic value through passive shading and policy frameworks should adapt to this dual-use reality. Integrating agronomic co-benefits into decommissioning policy supports long-term farm productivity and climate resilience. Full article

Climate change is expected to negatively impact agricultural production, leading to phenological and metabolic changes, increased water demands, diminished yields, and changed organoleptic characteristics, restricting the positive geographic productivity potential. As an adaptive strategy, agriculture in mountainous regions has gained prominence despite the fact that it entails new challenges. Indeed, mountain-specific conditions and limitations need to be considered, compared to the traditional productive regions. Consequently, there is a lack of information about the most suitable locations because the new conditions and limitations need to be accounted for. This study provides a crop suitability assessment approach to be used in mountainous regions where data about crop yield or development is scarce or nonexistent. Specifically, we evaluated the suitability of vineyards and apple orchards in the southern Pyrenees and Pre-Pyrenees. Using Geographical Information System (GIS) techniques, integrated with fuzzy logic and the Analytic Hierarchy Process (AHP), we combined traditional climatic, soil, and topographic indicators with factors relevant to mountainous regions. Our results indicated that the most suitable areas were primarily in lower basins and sunny hillsides, with smaller water needs. Vineyards would benefit from a very low risk of late spring frosts and elevated solar radiation, whereas apple orchards from a reduced risk of hailstorms, a very low risk of late spring frosts, and mild slopes. The fuzzy membership functions combined with the AHP facilitated the integration of indicators, effectively identifying areas with high potential for crop development. This approach contributes to landscape management and planning by offering a modifiable tool for assessing crop suitability in mountainous regions. Full article

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust. Full article

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of $44 °\mathrm{C}$, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

Balconies are widely recognized for enhancing urban livability, making them attractive elements to incorporate in building renovation projects. However, their impact on energy performance remains insufficiently studied, particularly in temperate climates, like the Mediterranean, where both heating and cooling demands must be considered. This article evaluates the energy impacts of integrating open balconies into housing retrofits on the space conditioning demand of dwellings through spatialized analysis at the urban block scale. Focusing on Barcelona’s Eixample district, a parametric Urban Building Energy Modeling (UBEM) was employed to assess how balcony design interacts with urban morphology (orientation, obstructions), building features (window-to-wall ratio, WWR), and balcony length. Results reveal a seasonal trade-off at the block scale: balconies increase heating demand (0.1–1.6 kWh/m²·yr) by reducing winter solar gain but decrease cooling demand (0.1–3.8 kWh/m²·yr) through summer shading. Net effects vary by unit position, with south-facing and moderately glazed dwellings benefiting the most. Deeper balconies (1.5–2 m) amplify both effects, while optimal depth depends on the window-to-wall ratio. Under future climates, retrofits combining insulation and balconies mitigate rising cooling demands more effectively than insulation alone, reducing block-level demand by up to 16%. Although balconies alone show modest energy savings at the block scale, they enhance localized thermal resilience. The study highlights the need for integrated retrofit strategies that balance thermal insulation with solar protection to address both current and future energy challenges while enhancing occupant well-being. Full article