Search Results (1,421)

As energy systems transition toward high shares of variable renewable generation, local energy communities (ECs) are increasingly relevant for enabling demand-side flexibility and self-sufficiency. This shift is particularly evident in the residential sector, where the deployment of photovoltaic (PV) systems is rapidly growing. While mixed-integer linear programming (MILP) remains the standard for operational optimization and demand response in such systems, its computational burden limits scalability and responsiveness under real-time or uncertain conditions. Reinforcement learning (RL), by contrast, offers a model-free, adaptive alternative. However, its application to real-world energy system operation remains limited. This study explores the application of a Deep Q-Network (DQN) to a real residential EC, which has received limited attention in prior work. The system comprises three single-family homes sharing a centralized heating system with a thermal energy storage (TES), a PV installation, and a grid connection. We compare the performance of MILP and RL controllers across economic and environmental metrics. Relative to a reference scenario without TES, MILP and RL reduce energy costs by 10.06% and 8.78%, respectively, and both approaches yield lower total energy consumption and CO${}_2$-equivalent emissions. Notably, the trained RL agent achieves a near-optimal outcome while requiring only 22% of the MILP’s computation time. These results demonstrate that DQNs can offer a computationally efficient and practically viable alternative to MILP for real-time control in residential energy systems. Full article

This research addresses the challenge of predicting operating vehicles’ speeds (V85) on single-carriageway road tangents. While most previous models rely on preceding segment speeds or focus on curves, this research develops an independent prediction model specifically for road tangents, based on empirical data collected in Croatia. A total of 46 locations across 23 road cross-sections were analysed, with operating speeds measured using field radar surveys and fixed traffic counters. Following a comprehensive correlation and multicollinearity analysis of 24 geometric, environmental, and traffic-related variables, a multiple linear regression model was developed using a training dataset (36 locations) and validated on a separate test set (10 locations). The model includes nine statistically significant predictors: shoulder type (gravel), edge line quality (excellent and satisfactory), pavement quality (excellent), average summer daily traffic (ASDT), crash ratio, edge lane presence, overtaking allowed, and heavy goods vehicle share. The model demonstrated strong predictive performance (R² = 0.89, RMSE = 5.24), with validation results showing an average absolute deviation of 2.43%. These results confirm the model’s reliability and practical applicability in road design and traffic safety assessments. Full article

To address the negative sample selection bias and limited interpretability of traditional debris-flow event susceptibility models, this study proposes a framework that enhances generalization by integrating negative sample screening via a stacking ensemble model with an interpretable random forest. Using Wenchuan County, Sichuan Province, as the study area, 19 influencing factors were selected, encompassing topographic, geological, environmental, and anthropogenic variables. First, a stacking ensemble—comprising logistic regression (LR), decision tree (DT), gradient boosting decision tree (GBDT), and random forest (RF)—was employed as a preliminary classifier to identify very low-susceptibility areas as reliable negative samples, achieving a balanced 1:1 ratio of positive to negative instances. Subsequently, a stacking–random forest model (Stacking-RF) was trained for susceptibility zonation, and SHAP (Shapley additive explanations) was applied to quantify each factor’s contribution. The results show that: (1) the stacking ensemble achieved a test-set AUC (area under the receiver operating characteristic curve) of 0.9044, confirming its effectiveness in screening dependable negative samples; (2) the random forest model attained a test-set AUC of 0.9931, with very high-susceptibility zones—covering 15.86% of the study area—encompassing 92.3% of historical debris-flow events; (3) SHAP analysis identified the distance to a road and point-of-interest (POI) kernel density as the primary drivers of debris-flow susceptibility. The method quantified nonlinear impact thresholds, revealing significant susceptibility increases when road distance was less than 500 m or POI kernel density ranged between 50 and 200 units/km²; and (4) cross-regional validation in Qingchuan County demonstrated that the proposed model improved the capture rate for high/very high susceptibility areas by 48.86%, improving it from 4.55% to 53.41%, with a site density of 0.0469 events/km² in very high-susceptibility zones. Overall, this framework offers a high-precision and interpretable debris-flow risk management tool, highlights the substantial influence of anthropogenic factors such as roads and land development, and introduces a “negative-sample screening with cross-regional generalization” strategy to support land-use planning and disaster prevention in mountainous regions. Full article

Energy shortage and environmental pollution have accelerated the adoption of lithium-ion batteries (LIBs) as efficient energy storage solutions. However, their performance and safety challenges under extreme temperatures highlight the urgent need for effective temperature control during charging and discharging, making a comprehensive understanding of electrochemical and thermal behaviors crucial. This paper develops a 3D electrochemical–thermal coupled model for 150 Ah lithium iron phosphate (LFP) batteries to investigate thermal behavior at varying charge–discharge rates. An integrated learning regression prediction system, incorporating a structured vector autoregression (SVAR) model, is subsequently proposed to analyze the interactions among multiple electrochemical and thermal variables. The temperature difference exceeds 5 °C at higher charging rates (1.3C, 1.5C), driven primarily by accelerated heat generation—especially reversible heat. Complex interactions exist between electrochemical and thermal parameters. When charging at 0.5C, voltage, current density, battery capacity, and the maximum temperature difference (MTD) are all significantly and positively correlated (p < 0.001). Under 1C discharge conditions, voltage exhibits a strong positive correlation with most thermal characteristic variables, and correlation coefficients across different operating conditions range from −0.9731 to 0.973. Finally, the proposed ensemble learning system exhibits excellent prediction accuracy, strong generalization, and robust trend analysis, with practical guiding value. Full article

The accurate short-term forecasting (PV) of power is crucial for grid stability control, energy trading optimization, and renewable energy integration in smart grids. However, PV generation is extremely variable and non-linear due to environmental fluctuations, which challenge the conventional forecasting models. This study proposes a hybrid deep learning architecture, Wavelet Transform–Transformer–Temporal Convolutional Network–Efficient Channel Attention Network–Gated Recurrent Unit (WT–Transformer–TCN–ECANet–GRU), to capture the overall temporal complexity of PV data through integrating signal decomposition, global attention, local convolutional features, and temporal memory. The model begins by employing the Wavelet Transform (WT) to decompose the raw PV time series into multi-frequency components, thereby enhancing feature extraction and denoising. Long-term temporal dependencies are captured in a Transformer encoder, and a Temporal Convolutional Network (TCN) detects local features. Features are then adaptively recalibrated by an Efficient Channel Attention (ECANet) module and passed to a Gated Recurrent Unit (GRU) for sequence modeling. Multiscale learning, attention-driven robust filtering, and efficient encoding of temporality are enabled with the modular pipeline. We validate the model on a real-world, high-resolution dataset of a Moroccan university building comprising 95,885 five-min PV generation records. The model yielded the lowest error metrics among benchmark architectures with an MAE of 209.36, RMSE of 616.53, and an R² of 0.96884, outperforming LSTM, GRU, CNN-LSTM, and other hybrid deep learning models. These results suggest improved predictive accuracy and potential applicability for real-time grid operation integration, supporting applications such as energy dispatching, reserve management, and short-term load balancing. Full article

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products. Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimization methods. This study proposes a dynamic optimization model for the storage location assignment problem, integrating both temperature and humidity constraints into the placement of stock-keeping units. The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse, contributing to the reduction in the energy consumption. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction in average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in a real-data context. The results show that the approach minimizing environmental stress consistently outperforms traditional methods in quality-related metrics, maintaining superior objective function values. Full article

This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas. The data encompasses a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, including contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a time series Transformer model. The results show that the Transformer model achieved the highest prediction accuracy for skid resistance (R² = 0.981), while Random Forest performed best for macrotexture prediction (R² = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is non-linear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning. Full article

Accurate temperature control across different zones during the combustion and heat exchange stages is crucial for both the economic efficiency of municipal solid waste incineration (MSWI) power plants and the consistent achievement of environmental targets. To address limitations in existing research, such as single-point temperature prediction models and the difficulty in characterizing the correlation mapping between adjacent zones, this article proposes a multi-point serial temperature prediction modeling method for the combustion and heat exchange stages of the MSWI process. Firstly, based on identifying five key temperature points across different zones in these stages, the Pearson correlation coefficient (PCC) is utilized for regional feature selection targeting each individual temperature point. Subsequently, multiple single temperature point prediction models based on a linear regression decision tree (LRDT) are constructed using the selected feature variables. Finally, considering the mutual influence between temperatures in neighboring zones, a serial multi-point temperature prediction model is built by using the knowledge transfer. To our knowledge, this is the first interpretable multi-point temperature prediction model for the MSWI process. It can assist in precise temperature control across different zones during the combustion and heat exchange stages in future studies. Validation results demonstrate that the minimum MSE attained 0.0238, the minimum MAE reached 0.1223, and the maximum R² achieved 0.9985 across multiple temperature points. The proposed method is validated using actual operational data from an MSWI power plant in Beijing. Full article

Chile, which accounts for 27% of global cherry exports (USD 2.26 billion annually), faces a critical industry challenge in crop health monitoring. While automated sensors monitor environmental variables, phytosanitary diagnosis still relies on manual visual inspection, leading to detection errors and delays. Given this reality and the growing use of AI models in agriculture, our study quantifies the theory–practice gap through comparative evaluation of three transfer learning architectures (namely, VGG16, ResNet50, and EfficientNetB0) for automated disease identification in cherry leaves under both controlled and real-world orchard conditions. Our analysis reveals that excellent laboratory performance does not guarantee operational effectiveness: while two of the three models exceeded 97% controlled validation accuracy, their field performance degraded significantly, reaching only 52% in the best-case scenario (ResNet50). These findings identify a major risk in agricultural transfer learning applications: strong laboratory performance does not ensure real-world effectiveness, creating unwarranted confidence in model performance under real conditions that may compromise crop health management. Full article

The development of reliable visual inference models is often constrained by the burdensome and time-consuming processes involved in collecting and annotating high-quality datasets. This challenge becomes more acute in domains where key phenomena are time-dependent or event-driven, narrowing the opportunity window to capture representative observations. Yet, accelerating the deployment of deep learning (DL) models is crucial to support timely, data-driven decision-making in operational settings. To tackle such an issue, this paper explores the use of 2D synthetic data grounded in real-world patterns to train initial DL models in contexts where annotated datasets are scarce or can only be acquired within restrictive time windows. Two complementary approaches to synthetic data generation are investigated: rule-based digital image processing and advanced text-to-image generative diffusion models. These methods can operate independently or be combined to enhance flexibility and coverage. A proof-of-concept is presented through a couple case studies in precision viticulture, a domain often constrained by seasonal dependencies and environmental variability. Specifically, the detection of Lobesia botrana in sticky traps and the classification of grapevine foliar symptoms associated with black rot, ESCA, and leaf blight are addressed. The results suggest that the proposed approach potentially accelerates the deployment of preliminary DL models by comprehensively automating the production of context-aware datasets roughly inspired by specific challenge-driven operational settings, thereby mitigating the need for time-consuming and labor-intensive processes, from image acquisition to annotation. Although models trained on such synthetic datasets require further refinement—for example, through active learning—the approach offers a scalable and functional solution that reduces human involvement, even in scenarios of data scarcity, and supports the effective transition of laboratory-developed AI to real-world deployment environments. Full article

Under cool and moist climates, timely implementation of ditch network maintenance (DNM) is crucial for sustaining productivity of drained forests, thus reducing operational costs, while mitigating environmental risks. This underscores the need to understand tree growth responses to DNM. This study evaluated the effects of DNM on tree radial increment in sites with both organic and mineral drained soils, focusing on regionally commercially important species: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and silver birch (Betula pendula). Responses of relative growth changes over eight years post-DNM to site and tree characteristics were assessed using a linear mixed-effects model. Species- and site-specific growth responses to DNM were indicated by significant interactions between tree species, site type, and distance from the drainage ditch. While growth responses were generally neutral (non-significant), variability among sites and species suggests that both organic and mineral soils might be prone to site-level moisture depletion near drainage infrastructure in the post-DNM period. The effect of stand age and density suggested higher responsiveness of older and less dense stands, hence positive effects of thinning to resilience of stands to DNM. These findings highlight the importance of adapting DNM strategies to local site conditions and stand characteristics to minimize drought-related growth limitations. Full article

Environmental pollution and climate change are significant challenges on the path to sustainable development for human society. This study employs panel data from 30 provinces in China, from 2000 to 2022, to empirically analyzes the impact of digital technology on the synergistic governance of pollution and carbon reduction, as well as its underlying mechanisms. The findings show that digital technology helps optimize corporate management models, enhance environmental governance capabilities, and create a green and low-carbon social atmosphere, thereby significantly improving the level of synergistic governance of pollution and carbon reduction. This conclusion remains robust after replacing the explanatory variable with digital patents, substituting regression models, and controlling for endogeneity issues. However, the effects of digital technology on synergistic governance of pollution and carbon reduction are heterogeneous. Digital technology has a more pronounced positive impact in regions with a solid foundation in environmental governance, distinct green economic characteristics, and mature digital operation models. The mediating effect results indicate that digital technology can achieve synergistic governance of pollution and carbon reduction through two pathways: improving energy efficiency and promoting virtual agglomeration. The conclusions drawn provide insights for relevant stakeholders to fully utilize digital technology to accelerate green production and address climate change. Full article

Carbon dioxide (CO₂) is the primary component contributing to anthropogenic greenhouse gas emissions, necessitating the adoption of effective mitigation strategies to promote environmental sustainability. Among the various carbon capture methodologies, chemical absorption is acknowledged as the most scalable solution for post-combustion applications. This investigation presents a thorough, comparative, and scenario-based evaluation of both singular and blended amine solvents for CO₂ capture within packed absorption–desorption columns. A validated rate-based model employing monoethanolamine (MEA) functions as the benchmark for executing process simulations. Three sequential scenarios are meticulously examined to switch the solvents and see the results. In the preliminary scenario, baseline performance is assessed by applying MEA to achieve the designated 73% removal target. Then the implementation of alternative solvents is examined—piperazine (PZ), a combination of methyldiethanolamine (MDEA) and PZ, and a blend of MEA and PZ—under uniform design parameters to ascertain their relative effectiveness and performance. In the second scenario, the design of the system is changed to reach a CO₂ removal efficiency for MEA of 90%, and then MEA is switched to other solvents. In the final scenario, critical design parameters, including column height and diameter, are adjusted for each solvent system that did not meet the 90% capture efficiency in Scenario 2 to achieve 90% CO₂ capture. A comprehensive sensitivity analysis is subsequently conducted on the adjusted systems to evaluate the influence of critical operational variables such as temperature, flue gas and solvent flow rates, and concentrations. Importantly, the MEA + PZ blend also demonstrated the lowest specific reboiler duty, as low as 4.28 MJ/kg CO₂, highlighting its superior energy efficiency compared to other solvents in the condition that the system in this study is pilot-scale, not commercial-scale, and due to this reason, the energy consumption of the system is slightly higher than the reported value for the commercial-scale systems. The results yield invaluable insights into the performance trade-offs between singular and blended amines, thereby facilitating the development of more efficient CO₂ capture systems that function within practical constraints. Full article

This study presents a novel switchable multi-inlet Building integrated photovoltaic/thermal (BIPV/T) curtain wall system designed to enhance solar energy utilization in commercial buildings. The system integrates controllable air inlets and motorized dampers that dynamically adjust airflow patterns in response to real-time environmental conditions such as solar irradiance, ambient air temperature, and PV panel temperature. A steady-state energy balance model, developed using a thermal network analogy and implemented in Python, was used to simulate winter operation in Montréal, Canada. Three operating modes with different air inlet configurations were assessed to evaluate system performance across variable air velocities and solar conditions. Results indicate that the switchable system improves combined thermal and electrical generation by 2% to 25% compared to fixed one- or two-inlet systems. Under low irradiance and air velocity, one-inlet operation is dominant, while higher solar gain and airflow favor two-inlet configurations. The system demonstrates effective temperature control and enhanced energy yield through optimized airflow management. This work highlights the potential of integrated control strategies and modular façade design in improving the efficiency of solar building envelope systems and offers practical implications for scalable deployment in energy-efficient, heating-dominated climates. Full article

Accurate characterization of surface solar irradiance at fine spatial, temporal, and spectral resolution is central to applications such as solar energy and environmental monitoring. On the one hand, modeling radiative transfer to achieve such accuracy requires detailed characterization of a wide range of factors, including the vertical profiles of gaseous and particulate absorbers and scatterers, wavelength-resolved surface reflectivity, and the three-dimensional morphology of clouds. On the other hand, satellite-based remote sensing products typically provide top-of-the-atmosphere irradiance at coarse spatial resolutions, where individual pixels can span several kilometers, failing to capture fine-scale intra-pixel variability. In this study, we introduce a machine learning framework that integrates large-scale remote sensing satellite data with hyperlocal, second-by-second ground-based measurements from an ensemble of low-cost spectral sensors to estimate the wavelength-resolved surface solar irradiance spectra at the hyperlocal level. The satellite data are obtained from the Harmonized Sentinel-2 MSI (MultiSpectral Instrument), Level-2A Surface Reflectance (SR) product, which offers high-resolution surface reflectance data. By leveraging machine learning, we model the relationship between satellite-derived surface reflectance and ground-based spectral measurements to predict high-resolution, wavelength-resolved irradiance, using target data obtained from an NIST-calibrated reference instrument. By utilizing a low-cost sensor ensemble that is easily deployable at scale, combined with downscaled satellite data, this approach enables accurate modeling of intra-pixel variability in surface-level solar irradiance with high temporal resolution. It also enhances the utility of the Harmonized Sentinel-2 MSI data for operational remote sensing. Our results demonstrate that the model is able to estimate surface solar irradiance with an R² ≈ 0.99 across all 421 spectral bins from 360 nm to 780 nm at 1 nm resolution, offering strong potential for applications in solar energy forecasting, urban climate research, and environmental monitoring. Full article