Search Results (126)

This study develops an explainable reinforcement learning (RL) control framework for hybrid ventilation in Mediterranean office buildings to enhance thermal comfort, energy efficiency, and long-term climate resilience. A working environment was created Using EnergyPlus to represent an office test cell equipped with natural ventilation and air conditioning. The RL controller, based on Proximal Policy Optimization (PPO), was trained exclusively on present-day Typical Meteorological Year (TMY) data from Beirut and subsequently evaluated, without retraining, under future 2050 and 2080 climate projections (SSP1-2.6 and SSP5-8.5) generated using the Belcher morphing technique, in order to quantify robustness under projected climate stressors. Results showed that the RL control achieved consistent, though moderate, annual HVAC energy reductions (6–9%), and a reduction in indoor overheating degree (IOD) by about 35.66% compared to rule-based control, while maintaining comfort and increasing natural ventilation hours. The Climate Change Overheating Resistivity (CCOR) improved by 24.32%, demonstrating the controller’s resilience under warming conditions. Explainability was achieved through Kernel SHAP, which revealed physically coherent feature influences consistent with thermal comfort logic. The findings confirmed that physics-informed RL can autonomously learn and sustain effective ventilation control, remaining transparent, reliable, and robust under future climates. This framework establishes a foundation for adaptive and interpretable RL-based hybrid ventilation control, enabling long-lived office buildings in Mediterranean climates to reduce cooling energy demand and mitigate overheating risks under future climate change. Full article

Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear interactions between meteorological drivers and yield formation in perennial fruit trees. To address this challenge, the study constructed a yield prediction framework using an optimized Random Forest (RF) model integrated with interpretable machine learning (SHAP), based on a comprehensive dataset from 17 major production regions in Hainan Province (2000–2022). The model demonstrated robust predictive capability at the provincial scale (R² = 0.564, RMSE = 2.1 t/ha) and high consistency across regions (R² ranging from 0.51 to 0.94). Feature importance analysis revealed that heat accumulation (specifically growing degree days above 20 °C) is the dominant driver, explaining over 85% of yield variability. Crucially, scenario simulations uncovered asymmetric climate risks across phenological stages: while moderate warming generally enhances yield by promoting vegetative growth and ripening, it acts as a stressor during the Fruit Development stage, where temperatures exceeding 26 °C trigger yield decline. Furthermore, the yield penalty for drought during Flowering (−8.09%) far outweighed the marginal benefits of surplus rainfall, identifying this window as critically sensitive to water deficits. These findings underscore the necessity of phenology-aligned adaptation strategies—specifically, securing irrigation during flowering and deploying cooling interventions during fruit development—providing a data-driven basis for climate-smart management in tropical agriculture. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article

Depending on the factors to which the soils are exposed, many properties and engineering parameters may change. In particular, the temperature parameter affects the strength of the soils, the degree of compressibility, permeability, void ratio, Atterberg limits, and many other parameters. In areas where high temperatures occur, such as heat piles and nuclear waste storage areas, alternative soil mixtures are needed that can stabilize or better optimize the behavior of the soils. For this purpose, additives with high heat transfer capacity and symmetry can be used. In this study, aluminum additive, which is known to have high conductivity, was used together with zeolite–bentonite mixtures. Aluminum-added mixtures were kept at different temperatures, and their thermal conductivity values were measured at the end of different periods. Measurements were first carried out at room temperature for all mixtures. Then, measurements were repeated at the end of 1, 3, and 10 days for 55 °C and 80 °C temperature values. At the end of the heating periods, the samples were left to cool to room temperature, and the thermal conductivity values were examined at the end of the heating–cooling cycle. Experimental results showed that thermal conductivity increased as temperature increased when the same period was taken as a basis, but an increase was observed for 1 and 3 day heating periods, while the thermal conductivity values for the 10th day decreased. The initial increase is attributed to the densification of the material due to the removal of free and weakly bound water or to the improvement of solid–solid contact paths. The subsequent decrease is due to microstructural deterioration, such as increased air-filled porosity, drying shrinkage, and microcracking due to thermal stresses, and material degradation caused by prolonged heating. In addition, thermal conductivity values of the mixtures under high temperature were estimated for days 100 and 365 using the DeepSeek method. The results showed that the thermal conductivity coefficients symmetrically decreased with increasing time. Full article

Humus is the core product and key indicator of compost maturity. How to improve the humus content and accelerate its formation in composting is critical for the improvement of compost quality. This study investigated the effects of adding compost derived from different stages including thermophilic, cooling, and maturation phases on compost initiation and efficiency in terms of humus formation and microbial community dynamics. The results reveal that adding compost from the cooling stage markedly outperforms the thermophilic and maturation phases, achieving a germination index of 107.22%, a carbon-to-nitrogen ratio of 15.95, a humus content of 91.12 g/kg, a humic acid concentration of 71.49 g/kg, and a polymerization degree of 3.64. EEMs indicated that the cooling-phase additive increased humic-like fluorescence (Region V) at day 35. The abundance and diversity of humifying bacteria were significantly enriched, and the succession of microbial community was accelerated as confirmed by redundancy analysis. This approach also improved compost quality and reduced the overall composting duration, thus suggesting that using compost from the cooling phase as an additive is an effective way to increase the humus content and accelerate the humification, providing a green solution for organic waste recycling and sustainable agricultural development and production. Full article

Rapid warming and expanding heat seasons are reshaping electricity demand in cities, with basin-type megacities like Chengdu facing amplified risks due to calm-wind, high-humidity conditions and fast-growing digital infrastructure. This study develops a Transformer-based, multi-model downscaling framework that integrates outputs from 17 CMIP6 global climate models (GCMs), dynamically re-weighted through self-attention to generate city-scale temperature projections. Compared to individual models and simple averaging, the method achieves higher fidelity in reproducing historical variability (correlation ≈ 0.98; RMSD < 0.05 °C), while enabling century-scale projections within seconds on a personal computer. Downscaled results indicate sustained warming and a seasonal expansion of cooling needs: by 2100, Chengdu is projected to warm by ~2–2.5 °C under SSP2-4.5 and ~3.5–4 °C under SSP3-7.0 (relative to a 2015–2024 baseline). Using a transparent, temperature-only Cooling Degree Day (CDD)–load model, we estimate median summer (JJA) electricity demand increases of +12.8% under SSP2-4.5 and +20.1% under SSP3-7.0 by 2085–2094, with upper-quartile peaks reaching +26.2%. Spring and autumn impacts remain modest, concentrating demand growth and operational risk in summer. These findings suggest steeper peak loads and longer high-load durations in the absence of adaptation. We recommend cost-aware resilience strategies for Chengdu, including peaking capacity, energy storage, demand response, and virtual power plants, alongside climate-informed urban planning and enterprise-level scheduling supported by high-resolution forecasts. Future work will incorporate multi-factor and sector-specific models, advancing the integration of climate projections into operational energy planning. This framework provides a scalable pathway from climate signals to power system and industrial cost management in heat-sensitive cities. Full article

The aim of the study is to analyze the impact of climate change on modern low-energy construction. The authors attempted to answer the question whether an existing single-family building that meets the current requirements for a low-energy facility can be called such in terms of ongoing long-term climate changes. Therefore, on the model of the building in question, the ESP-r program analyzed the impact of climate change on energy consumption for both heating and cooling purposes. The SSP2-4.5 scenario (RCP 4.5 according to the IPCC 5th Assessment Report) was adopted, generating future climate parameters for 2050 and 2080, using the HadCM3 model. In order to validate the model, the actual energy consumption values were compared with the values obtained from numerical modeling in the ESP-r program. The final task was to analyze the impact of ongoing climate changes on energy parameters and comfort of use of the facility. Based on the results obtained, the authors concluded that the effect of the changes that take place is the need to introduce an air conditioning system into it in the summer, because the currently existing solution, using a mechanical ventilation system to maintain thermal comfort, is unable to provide the required parameters in the rooms. We are dealing with the phenomenon of excessive temperature increase in the building in the summer. Therefore, the facility currently designed as a low-energy building will require the installation of additional installation systems in the coming years, primarily cooling rooms, which will involve increased energy consumption. Full article

This study investigates the cooling potential of night ventilation and the climate adaptability of local vernacular buildings in the Turpan basin, aiming to identify passive energy-saving design strategies. A rural building with an air-drying shelter was selected for summer indoor environment measurements (two stages: all-day window closure vs. night ventilation), and a numerical model was established to simulate the impacts of window-to-wall ratio and window shading projection factor on the indoor environment. Results indicate that night ventilation introduces cool outdoor air to replace indoor hot air, cools building components, improves thermal comfort, and reduces cooling energy demand. Without additional cooling technology, increasing the window-to-wall ratio lowers nighttime temperatures but increases Degree Discomfort Hours, while appropriately sized shading devices mitigate daytime overheating from larger windows. Benefiting from the high thermal storage capacity of earth-appressed walls, semi-underground rooms offer better comfort with lower temperatures and higher humidity; for aboveground rooms, orientation is critical due to intense solar radiation. The air-drying shelter reduces solar radiant heat absorption and inhibits convective/radiative heat transfer on the roof’s external surface, significantly lowering its temperature from noon to midnight. This leads to notable reductions in the roof’s internal surface temperature (1.02 °C in the sealed stage, 2.09 °C during night ventilation) and the average indoor temperature (1.70 °C). Full article

Social housing in tropical regions faces critical thermal comfort challenges that will intensify under future climate change, yet current design practices lack systematic frameworks for evaluating long-term performance across multiple climate scenarios. This study assesses the thermal performance of social housing in southeastern Mexico using energy simulation, supervised machine learning, and global sensitivity analysis. Two housing typologies (single-story and two-story) were modeled across four cities (Mérida, Campeche, Cancún, and Tuxtla Gutiérrez) under climate change scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2100. Various machine learning models were trained to predict comfort temperature and cooling degree days. Regression Trees demonstrated superior performance, with R² values exceeding 0.98 for both thermal comfort indicators, achieving RMSE values of 0.0095 °C for comfort temperature and 0.2613 °C for cooling degree days. Global sensitivity analysis using the PAWN method revealed that ambient temperature was the most influential variable, accounting for 45–49% of the total sensitivity, followed by solar radiation (17–22%) and relative humidity (10–12%), while building-specific parameters had modest impacts (0.6–3.8%). Geographic variations were significant, with Mérida and Campeche showing higher cooling demands than Cancún and Tuxtla Gutiérrez. Future climate projections indicate substantial increases in cooling requirements by 2100, with CDD values expected to increase by approximately 40–50% under the RCP 8.5 scenario compared to current conditions. This research presents a computational framework for assessing thermal comfort in social housing, providing evidence-based insights for climate-adaptive building strategies in tropical regions. Full article

The temperature-based indicators heating and cooling degree days, are frequently utilized to quantitatively link indoor energy demand and outdoor thermal conditions, especially in the context of climate change. We present a comprehensive study of the heating and cooling degree-days and the degree-days categories for the near past (1976–2005), and the AR5 RCP4.5 and RCP8.5 scenario-driven future (2066–2095) over Southeast Europe based on an elaborated methodology and performed using a 19 combinations of driving global and regional climate models from EURO-CORDEX with horizontal resolution of 0.11°. Alongside the explicit focus of the degree-days categories and the finer grid resolution, the study benefits substantially from the consideration of the monthly, rather than annual, time scale, which allows the assessment of the intra-annual variations of all analyzed parameters. We provide evidences that the EURO-CORDEX ensemble is capable of simulating the spatiotemporal patterns of the degree-days and degree-day categories for the near past period. Generally, we demonstrate also a steady growth in cooling and a decrease in heating degree-days, where the change of the former is larger in relative terms. Additionally, we show an overall shift toward warmer degree-day categories as well as prolongation of the cooling season and shortening of the heating season. As a whole, the magnitude of the projected long-term changes is significantly stronger for the ’pessimistic’ scenario RCP8.5 than the ’realistic’ scenario RCP4.5. These outcomes are consistent with the well-documented general temperature trend in the gradually warming climate of Southeast Europe. The patterns of the projected long-term changes, however, exhibit essential heterogeneity, both in time and space, as well as among the analyzed parameters. This finding is manifested, in particular, in the coexistence of opposite tendencies for some degree-day categories over neighboring parts of the domain and non-negligible month-to-month variations. Most importantly, the present study unequivocally affirms the significance of the anticipated long-term changes of the considered parameters over Southeast Europe in the RCP scenario-driven future with all subsequent and far-reaching effects on the heating, cooling, and ventilation industry. Full article

Climate change necessitates the exploration of new, cooler viticultural regions globally. Chardonnay is an early ripening variety which is subjected to temperature extremes. This study aimed to investigate the response of Chardonnay in cool climatic regions in the Cape South Coast region of South Africa over two growing seasons in 2021–2022 and 2022–2023 in three commercial vineyards. An evaluation of the climatic, vegetative and reproductive characteristics was performed. Seasonal variation was the biggest driver of the Growing Degree Days (GDD) at the sites. Overall, the 2021–2022 season was warmer than the 2022–2023 season, but the microclimatic conditions were impacted by the cultivation practices which were applied. The canopy density and total leaf surface varied between the different sites (p < 0.01) and by season × site (p < 0.05). Site and the site × season interaction were the main drivers of the environmental conditions and cultivation practices. Canopy characteristics impacted the sugar accumulation rate over the two seasons. Grape berry transpiration was impacted by the environmental conditions at the sites. Chemical composition varied with soil depth. From the results of our study, although Chardonnay is suitable for cultivation in the Cape South region, site-specific conditions impact fruit development and the quality at harvest. Full article

This study examines ground surface and air temperatures and their implications for periglacial activity in the Țarcu Massif, Southern Carpathians, where data on current dynamics and climate responses remain scarce despite widespread periglacial landforms. To address this, we deployed seven temperature loggers between 2018 and 2024 across a range of periglacial landforms, including non-sorted patterned ground, a periglacial hummock, protalus rampart, block stream, periglacial tor, ploughing boulder, and nival niche. We analyzed key thermal indicators such as freeze–thaw cycles, freezing and thawing degree days, frost weathering intervals, frost days, and winter equilibrium temperatures—in relation to long-term air temperature records (1961–2023), snow cover dynamics, and local topographic and substrate conditions. Results reveal a marked warming trend at the Țarcu meteorological station, particularly after 1995, along with a shift in net thermal balance beginning in the late 1990s. Since then, climatic conditions at this site have no longer been favorable for the persistence of sporadic permafrost. Ground thermal conditions varied spatially, with coarse debris sites and rock wall maintaining the lowest MAGST values—typically with 1 to 2.5 °C cooler than fine-grained sediments—and the highest potential for frost-related weathering. Despite low and variable freeze–thaw cycle frequency, the high number of frost days (around 200 per year) and sustained frost weathering potential—exceeding 50 days annually at key sites—indicate that periglacial conditions remain active for nearly half the year around 2000 m in the Southern Carpathians. Snow cover dynamics proved to be a major control on ground thermal behavior, with earlier melting and delayed onset shortening its duration but amplifying early winter cooling. These findings indicate that the Țarcu Massif is a transitional periglacial environment, where active and relict features coexist under growing climatic pressure. The ongoing decline in frost-driven processes highlights the vulnerability of mid-latitude mountain periglacial systems to climate warming and underscores the need for continued monitoring to better understand future landscape evolution in the Southern Carpathians. Full article

The Life Cycle Cost Assessment (LCCA) methodology is widely used to determine the optimal thickness of thermal insulation for walls and roofs. The results depend on several factors, such as the degree day calculations method, the ambient or sol–air temperature, base temperature variations, and the heat capacity of the thermal envelope elements. This study aims to analyze the impact of solar radiation on mass walls with different orientations in five cities in northern Chile, which have severe solar climates. The goal is to determine the optimal thickness of expanded polystyrene insulation using the LCCA method, considering solar radiation, a varying base temperature, and validating results by analyzing the energy demand for heating and cooling of a typical house. The findings show that excluding solar radiation in the LCCA methodology can lead to an underestimation of the optimal insulation thickness by 21–39% for walls in northern Chile. It was also found that using variable monthly threshold temperatures for heating and cooling based on the adaptive thermal comfort model results in a slight underestimation (1–3%) of the optimal thickness compared to a constant annual temperature. An energy simulation of a typical house in five cities in northern Chile showed that neglecting the effect of solar radiation when determining the thermal insulation thickness for the studied wall can lead to a minor increase in heating and cooling energy demand, ranging from approximately 1% to 9%. However, this study emphasizes the importance of applying optimal insulation thickness for cities with more continental climates like Santiago and Calama, where the heating demand is higher than cooling. Full article

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. Full article

Despite the abundance of solar resources and significant electrical demand during the daytime, residential PV installations are rarely found in Saudi Arabia due to unfavorable economics, resulting from low electricity tariffs by global standards. This work reports on opportunities and challenges of residential PV installation in Saudi Arabia based on the deployment process and analyses of the performance of two 15 kWp PV systems installed on the rooftops of two similar villas in Jeddah, Saudi Arabia. For each villa, 18 months of electrical consumption and ambient temperature were available pre-installation, followed by 24 months of post-installation PV system monitoring, including incident radiation, generation, and import from the grid. A linear model of the consumption of the villas fitted between 0.016 and 0.019 kWh/m² per cooling degree day, with varying levels of interception. No significant change was observed post-installation of the PV system. On average, the reduction in overall electrical import from the grid was 20–30%. A financial analysis based on the real costs and performance of the installed systems found that the net billing feed-in tariff should be increased to SAR 1.0–1.5 (USD 0.27–0.40), depending on a range of other possible measures, in order to stimulate the growth in residential rooftop PVs. Full article