Search Results (24,533)

The absorber layer thicknesses of tandem photovoltaic devices containing hybrid organic–inorganic lead halide perovskite absorbers are optimized under AM 1.5 and AM 0 solar irradiance using external quantum efficiency (EQE) simulations. Using the EQE modeling approach derived from analysis of ellipsometric spectra collected from complete single-junction perovskite, all-perovskite tandem, and copper indium gallium diselenide (CIGS) thin film solar cells, structural–optical models are developed for two high-efficiency tandem solar cell configurations from their published EQE spectra. These configurations include a superstrate all-perovskite device and a substrate perovskite/CIGS device. These models serve as realistic and practical baselines for optimizing device performance under different circumstances. By increasing the thicknesses of an all-perovskite tandem superstrate device’s wide E_g and narrow E_g absorber layers from 350 and 975 nm to 356 and 1200 nm, the J_sc may be increased from 15.81 to 15.94 mA/cm² under AM 1.5 illumination. This corresponds to a potential increase in efficiency from 25.83 to 26.05% when using reported open circuit voltage (V_oc) and fill factor (FF). Under AM 0, an increase in absorber layer thickness to 310 and 1200 nm increases the J_sc from 18.56 to 19.72 mA/cm², which corresponds to an increase in efficiency from 30.33 to 32.22%. By increasing the thickness of the perovskite layer in a perovskite/CIGS substrate device from 500 to 615 nm, the J_sc may be increased from 18.84 to 19.65 mA/cm² assuming AM 1.5 illumination. This change would increase efficiency from 23.74 to 24.76%. Under AM 0 illumination, an increase in the perovskite thickness to 512 nm results in an increase in predicted J_sc from 23.13 to 23.34 mA/cm². This corresponds to a predicted efficiency increase from 29.15 to 29.41%. This modeling approach provides a stable platform for practical evaluation of different superstrate and substrate design tandem solar cells with perovskite semiconductors as at least one of their absorber layers. Full article

To address seed contamination, narrow-row mechanized cutting difficulties, and potential damage to maternal plants in muddy paddy fields during hybrid rice seed production, a walk-behind self-propelled hybrid rice male parent pulverizing and cutting machine was designed. The machine primarily consists of three key structures: a key cutting device, a gravity-free crop dividing device, and a crawler walking mechanism. The cutting device features an innovative mechanism where main-shaft rotation drives flail blades into inertial autorotation, while a stopper bar physically constrains their maximum swing amplitude to guarantee a 500 mm working width. Crucially, the gravity-free crop dividing device safely pushes aside adjacent maternal plants to effectively prevent accidental mechanical injury. A flexible plant model and a kinematic model were established using DEM software EDEM 2024. A three-factor, three-level orthogonal experiment indicated that the primary order of influence on the male parent cutting rate is forward speed > flail-blade rotational speed > blade arrangement. The optimal simulation parameters were a 0.4 m/s forward speed, a 1700 r/min blade rotational speed, and a straight–curved blade arrangement, yielding a simulated cutting rate of 97.60%. Furthermore, field tests demonstrated that under these optimal parameters, influenced by complex paddy conditions and natural plant lodging, the actual average cutting rate was 91.39%. The machine exhibited excellent passability and pulverizing performance, thoroughly satisfying the requirements of agronomic and agricultural machinery integration. Full article

Today, the use of increasingly ubiquitous synthetic media, or ‘deepfakes’, has become a risk to online trust, information integrity and individual security and is being created by artificial intelligence (AI). The current approaches are mainly based on either spatial features of CNNs or high-level semantic representations of Vision Transformer; both have major drawbacks in effectively leveraging multi-domain forensic cues. This paper presents FAViT (Frequency-Aware Vision Transformer), a hybrid architecture capable of jointly utilizing spatial- and frequency-domain forensic information by the means of a bidirectional cross-attention fusion scheme. We use an 11-channel forensic tensor in each face image (including per-channel Fast Fourier Transform (FFT) magnitude maps, Discrete Wavelet Transform (DWT) sub-bands, channel noise residual maps, Sobel gradient magnitude and channels of Error Level Analysis (ELA)). A Frequency Branch CNN processes this multi-domain tensor and the original RGB image is encoded with a pretrained ViT-B/16 spatial branch. The two streams are combined through the bidirectional cross-attention which allows the model to localize both spatial and spectral manipulation artifacts. We also present an adversarial cleaning simulation pipeline which partitions the training process with five post-processing attack methods, namely GFPGAN neural face restoration, learned autoencoder cleaning, etc., to increase resistance to real-world forensic defenses. Tests of FaceForensics++ C23 (7926 images, consisting of four manipulation types) show that FAViT attains F1-score of 86.22, AUC-ROC of 94.26 and accuracy of 85.55 on the held-out test set. The strength analysis of 21 attack conditions shows that the max degradation in AUC is 30.3, with specific strengths in GFPGAN restoration (AUC = 98.51). Robustness is evaluated based on 21 post-processing attack cases that include JPEG compression, Gaussian blurring, down-sampling, and GFDGAN neural-based restoration; it should be noted that robustness against gradient-based adaptive attacks requires additional attention. Testing on the CIFAKE and Celeb-DF v2 datasets reveals some limitations of domain generalization. Full article

Sustainable rural cultural tourism requires effective approaches to improving the visibility, accessibility, and decision feasibility of dispersed cultural resources, particularly in destinations where service information is fragmented across online and offline channels and tourists face substantial uncertainty in coordinating transport, accommodation, and cultural experiences. This study examines how artificial intelligence (AI) agents can support the sustainable digital transformation of rural cultural tourism by alleviating information asymmetry, releasing latent tourism demand, and facilitating calibrated human–machine trust. Drawing on human–machine trust theory and the Stimulus–Organism–Response framework, this study conceptualizes AI agent functionality through three dimensions: AI Information Quality (AIQ), Information Extensibility (IE), and AI Planning Autonomy (APA). Travel Planning Risk Awareness (TPRA), Human–Machine Trust (HMT), Planning Satisfaction (PS), Rural Cultural Tourism Attractiveness (RCTA), and Rural Cultural Tourism Intention (RCTI) are further incorporated into an integrated model comprising four pathways: information empowerment, autonomy–risk awareness tension, trust boundary, and demand release. Using the Ctrip AI Travel Assistant as the research context, 413 valid questionnaire responses were analyzed through a hybrid Structural Equation Modeling–Artificial Neural Network approach. The results support 12 of the 14 hypotheses. AIQ significantly influences IE (β = 0.530), PS (β = 0.304), and HMT (β = 0.380). HMT functions as a central mechanism connecting AI empowerment with tourism decision-making and exerts the strongest effect on RCTA (β = 0.485), reaching 100% normalized importance in the corresponding ANN model. TPRA positively affects HMT (β = 0.262), indicating that risk awareness can facilitate rational and calibrated trust rather than simply inhibiting AI acceptance. RCTA (β = 0.281) and PS (β = 0.218) jointly promote RCTI through the complementary mechanisms of destination pull and planning push. The findings demonstrate that AI agents can contribute to the sustainable development of rural cultural tourism by improving information accessibility, strengthening responsible human–AI collaboration, and transforming fragmented cultural resources into credible and actionable travel-planning options. This study provides implications for sustainable destination marketing, responsible AI travel-service design, rural revitalization, and the long-term development of rural cultural tourism, while clarifying trust as a psychological gate in AI empowerment. Full article

Evacuated-tube solar collectors charged with hybrid nanofluids can raise thermal output, but their coupled thermal and hydraulic response depends on many interacting variables, making the design an optimisation rather than a prediction problem. This study recasts it as a constrained, mixed-variable, three-objective task that maximises thermal efficiency and the Nusselt number while minimising pumping power over the hybrid pair, base fluid, weight fraction, component-one share and flow rate, and develops a memetic metaheuristic: the Adaptive Memetic Hybrid (AMH). A histogram gradient-boosted surrogate trained on 54,432 reduced-order runs, with held-out coefficients of determination of at least 0.9999, provides a fast screen, while a continuous reduced-order model validated to within 0.02 percent serves as the objective; the surrogate is accurate off-grid for efficiency but not for pumping power or the Nusselt number. Nine optimisers, comprising four baselines, three recent metaheuristics, and two AMH variants, were validated on twelve ZDT, DTLZ, and constrained problems over thirty trials using the hypervolume, generational distances, and spacing, and analysed with Friedman, Nemenyi, and Holm-corrected Wilcoxon tests. AMH attained the best mean Friedman rank of 3.08 (chi-square 65.7, p = 3.6 × 10⁻¹¹), significantly outperforming the recent methods and NSGA-III and remaining competitive with the strongest classical algorithms. On the collector, the reduced-order front recovers the 1512-design brute-force maximum efficiency to within 0.02 percent and improves the trade-off through continuous flow rates. The study is a deterministic, model-based optimisation process: the surrogate serves as a tool for fast screening and diagnostics, while the reconstructed reduced-order model is the objective for the final continuous optimisation. The collector application has a low effective design dimension, being governed mainly by the base fluid and the loop flow rate, so the decisive separation of the algorithms is established on the benchmark suite rather than on the collector. Experimental validation remains a task for future work. Full article

To effectively address the challenges of complex spatiotemporal conflicts, dynamic obstacle avoidance, and coordinated multi-objective optimization in preemptive multi-UAV logistics delivery within complex airspace, this study proposes a Hybrid Improved Multi-Objective Cuckoo Search algorithm (HI-MOCS) for preemptive multi-UAV cooperative logistics scheduling and planning. To overcome the limitations of conventional MOCS, including a low proportion of feasible solutions under complex constraints, susceptibility to local optima, and uneven distribution of multi-objective solution sets, a multi-constraint physical model and a multidimensional evaluation framework are established for preemptive scheduling. A positive knowledge-transfer mechanism based on the co-evolution of primary and auxiliary populations is developed, in which constraint-violation information is used to guide infeasible solutions toward the feasible region. A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population. In addition, a nonlinear dynamic adaptive parameter-adjustment strategy is designed to balance global exploration and local exploitation, while an iterative truncation-based environmental selection mechanism using the shortest-distance criterion is employed to improve the distribution quality of the Pareto solution set. The experimental results show that, in the benchmark scenario, HI-MOCS achieves an average increase of 33.26% in the total order completion rate and an average reduction of 15.34% in emergency response time compared with 11 multi-objective optimization algorithms, while also exhibiting favorable performance in terms of flight distance per completed order. The fleet-size analysis shows that the 15-UAV configuration achieves the lowest best mean fitness. The safety-distance analysis indicates that, compared with the other safety-distance settings, the 30 m setting increases the total order completion rate by an average of 26.55%, while reducing emergency response time and flight distance per completed order by 27.36% and 33.72%, respectively. The task-scale analysis shows that the 50-order scenario achieves the lowest best mean fitness. Further ablation experiments demonstrate that, compared with the average performance of MOCS and the four single-strategy variants, the complete HI-MOCS improves the total order completion rate by 20.27%, while reducing emergency response time and flight distance per completed order by 20.71% and 36.18%, respectively. The HV, IGD, and Pareto-front results further confirm that the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set. The current study is still validated under simulation conditions assuming reliable GNSS positioning and communication links, without explicitly considering communication delays. Full article

This work presents a covariance-aware extension of the hybrid nonlinear-kicker (NLK) injection framework. While previous baseline studies established centroid action reduction and hardware-aware NLK placement for idealized beams, the present study addresses finite-beam optimization using a realistic, position-dependent eight-wire kick profile. A realistic NLK field not only corrects the injected-beam centroid but also induces an amplitude-dependent shear across the internal phase-space ellipse. Depending on the injected covariance and ellipse orientation, this shear can either reduce or enhance beam filamentation. To exploit this mechanism, the AC-septum angle is treated as a trajectory-control knob for the injected centroid, while transfer-line matching is used to control the injected covariance matrix. We use ID2-downstream as the primary optics-favored case, as its high ${\beta }_N$ simultaneously increases nonlinear-field sampling and reduces the required correction kick. Furthermore, at this location, the injected beam is close to a matched, near-upright covariance condition (${\alpha }_{beam}\approx 0$). We also examine ID1 middle and ID1 downstream as trajectory-controllable alternatives, and formulate a combined optimization over septum angle, transfer-line optics, and NLK current. Operational measurements of AC-septum stability, first-turn trajectory reproducibility, septum response, and injection-bump response validate the available trajectory-control authority required for realistic NLK optimization. The analysis identifies a residual oscillation floor proportional to the square of the remaining centroid offset and inversely proportional to the local beta function, demonstrating that the physically relevant objective is minimization of the final mean action after the realistic NLK kick, rather than percentage action reduction alone. This framework establishes a practical optimization strategy under realistic aperture, stability, and hardware constraints. In a representative covariance-matched ID2D case, the realistic eight-wire NLK reduces both mean horizontal action (from 3.372 μm to 0.382 μm) and finite-beam spread contribution (from 0.475 μm to 0.039 μm). Operationally, first-turn rms trajectory variations remain under 0.4 mm, and the measured trajectory response of 19.4 mm/mrad closely matches the 19.8 mm/mrad model. Full article

Robotic Aerial Work Platforms (RAWPs) are subjected to multi-source excitations including wind gusts and inertial loads, which result in strongly nonlinear and time-varying coupled triaxial attitude vibrations. Conventional recurrent architectures such as LSTM and GRU suffer from gradient vanishing when processing long sequences, while Transformer utilize self-attention mechanisms to learn simple periodic correlations; however, the vibrations in RAWPs exhibit a complex time-series pattern composed of low-frequency oscillations superimposed with high-frequency impacts and accumulates errors through autoregressive decoding. To address these limitations, this paper proposes an improved Transformer model featuring dual-channel periodic positional encoding and global–local hybrid multi-head attention for one-shot multi-step long-sequence prediction of RAWPs attitude vibrations. The proposed method designs a dual-channel independent sine–cosine positional encoding with a tunable periodic modulation factor to explicitly embed the multi-scale periodicity priors of vibration signals and introduces a global–local hybrid attention mechanism that parallelly extracts transient amplitude impact features in the time domain and periodic fluctuation features in the frequency domain. A full-scale aerial experimental platform is established to collect triaxial vibration data under two operating conditions at a sampling frequency of 20 Hz. The results determine the optimal periodic modulation factor and input window length, and ablation studies validate the synergistic gains of the two proposed modules. Comparative results demonstrate that the proposed model achieves substantially reduced prediction errors. In terms of pitch angle, the proposed model achieves a performance improvement of 53.89% over Transformer, 52.11% over LSTM, and 57.67% over GRU. The proposed model effectively provides a reliable data-driven prediction framework for attitude monitoring and active vibration suppression of aerial work platforms. Full article

Photogrammetric 3D city models contain detailed planimetric and elevation information that supports urban visualization and low-altitude applications. However, the direct dissemination of high-accuracy models may expose sensitive geometric measurements. Existing protection methods mainly focus on conventional encryption, coordinate scrambling, or two-dimensional data perturbation and do not adequately balance geometric accuracy degradation with the visual usability of textured 3D meshes. This study proposes a controlled geometric deformation method that processes the planimetric and elevation components independently. In the horizontal domain, a normalized Sigmoid function generates smooth, bounded, and spatially varying coordinate displacements. In the vertical domain, a normalized deformation function combines global elevation stretching with amplitude-constrained sine-wave superposition. The sine-wave parameters are generated using a seed-sensitive hybrid cascaded chaotic system, producing reproducible but model-dependent nonlinear deformation patterns. During processing, the mesh connectivity, face indices, texture coordinates, texture images, and material relationships remain unchanged. The method was evaluated using low-rise and high-rise photogrammetric 3D scenes with different horizontal extents and elevation characteristics. Under the selected 10 m planimetric and 5% elevation settings, the mean planimetric displacements were 10.474 and 10.045 m, while the relative elevation deformations were 5.01% and 5.30%, respectively. Both datasets maintained monotonic elevation relationships and achieved 100% direction consistency. Their spatial-shape coefficients deviated from the corresponding reference values by only 0.02% and 1.33%. The results demonstrate that the proposed method provides controllable and spatially continuous geometric deformation while maintaining mesh connectivity, overall morphology, and visual interpretability. It can therefore serve as a practical pre-processing approach for the risk-reduced dissemination and non-measurement-oriented visualization of photogrammetric 3D city models. Full article

Problems with power transformers reduce grid reliability and can lead to large financial losses. Traditional Dissolved Gas Analysis (DGA) methods, such as the Key Gas Method, Ratios, and Duval Triangle, often give unclear results when faults are complex or occur together. This study introduces a Statistically Guided Decision Tree (SGDT) framework, a combined approach that uses statistical DGA analysis and decision tree learning to identify transformer faults. This approach creates rule-based fault categories using advanced statistical analysis of real DGA data and tests how well these categories work with a Decision Tree model. Advanced statistical techniques such as dispersion and association metrics, confidence intervals, and distribution characteristics were used on a wide range of DGA records collected from a 275 kV transformer to define threshold values. Thereafter, fault classification rules were developed, and finally, a decision tree algorithm was developed to evaluate whether the gas concentration-based rules for fault labelling aligned with real data behaviour. The classification accuracy of 0.993 was achieved, indicating a high rate of correctly identified fault types. The F1-score, representing the harmonic mean of precision and recall, was 0.980, confirming both high precision and recall. Specifically, the recall was 0.980, meaning that 98% of real fault cases were correctly found, while the precision was 0.981, showing that 98.1% of predicted fault cases were correct. The Area Under the Curve (AUC) was 0.987, showing the model could clearly tell the difference between fault and non-fault cases. This work demonstrates the effectiveness of the current proposed SGDT framework, and this will help utilities that want to digitise their transformer maintenance and diagnostics for better decision-making. Full article

The building sector faces an urgent challenge in balancing carbon neutrality goals with natural resource conservation. This study constructs a three-dimensional dynamic coupling model integrating Life Cycle Assessment, carbon footprint, and emergy analysis to evaluate the sustainability of a hydrogen–solar–geothermal hybrid energy system for an ecological office building in China’s hot summer and cold winter climate zone over a twenty-year horizon. The model incorporates dynamic factors including grid decarbonization, equipment efficiency degradation, and replacement cycles to overcome the systematic bias inherent in static LCA. Results reveal a significant trade-off: the hybrid system achieves a 29.8% reduction in global warming potential with a seven-year carbon payback period, yet non-renewable resource consumption doubles and resource scarcity damage increases by 173%. The carbon payback trajectory exhibits non-monotonic fluctuation, with electrolyzer replacement in year ten generating 360 tonnes of additional emissions that nearly reset the cumulative net value to zero. Multi-objective optimization identifies photovoltaic capacity as the system baseline (170–210 kW) and electrolyzer capacity as the primary regulating variable (35–62 kW), with the TOPSIS-recommended compromise solution of 200 kW photovoltaic, 50 kW electrolyzer, 30 kW fuel cell, and 32 m³ hydrogen storage achieving annual carbon emissions of 280 tonnes and a 33.3% reduction. Carbon pricing exhibits a nonlinear leverage effect with an incentive threshold of 200 RMB per tonne, substantially above China’s current 60–80 RMB per tonne level. This study concludes that while hydrogen–solar–geothermal hybrid systems offer substantial climate benefits, their comprehensive sustainability depends on proactive management of material scarcity costs, precise planning of equipment replacement cycles, and coordinated multi-level policy instruments. The findings provide methodological foundations for transitioning building carbon neutrality assessment from static LCA to dynamic coupling frameworks and from single carbon metrics to integrated carbon-resource-cost evaluations. All quantitative results presented herein are derived from this specific case study under the stated assumptions and parameter values; generalization to other building types or climate zones requires recalibration. Full article

Mobile networks have become targets of sophisticated cyber attacks. Critical vulnerabilities persist in the General Packet Radio Service Tunneling Protocol (GTP). Signature-based Intrusion Detection Systems (IDS) are inadequate against zero day exploits and novel attack patterns, necessitating more adaptive approaches. We propose GTP-AEGIS (Adaptive Ensemble with Gated Input Selection), a hybrid IDS that integrates signature-based detection with a CatBoost gradient boosting classifier via a Selective Heterogeneous Ensemble (SHE) framework. An Input-Dependent Confidence Gate (IDCG) applies a per-sample priority rule over CatBoost, a NearestCentroid Rule Engine (NCRE), and a signature pathway. A real Suricata engine detects attacks with 100% precision but only 69.2% binary recall when run standalone; within the ensemble, the signature role is played by an idealized Signature-Detection Surrogate (SDS), so the reported ensemble gains are upper bounds. On the evaluated GTP-U dataset, GTP-AEGIS reaches accuracy above 90% with 10% of the training data and raises recall for the rare invalid-TEID class from 48.9% to 64.4%; this improvement comes from the signature pathway rather than the NCRE, and the aggregate accuracy gain is not statistically significant after correction for multiple comparisons. All accuracies are obtained under a packet-level split, which a group-aware comparison shows to be optimistic by approximately 19 percentage points. The model flags 81 to 100% of packets from unseen attack families as non-normal, although this does not constitute unknown-class recognition. We report the limits of signature-only detection and of packet-level evaluation alongside the gains. Full article

In the present work, an alternative methodology was developed for the rapid prediction of pressure distributions over wings of low-speed aircraft. A hybrid neural network architecture, named “MARTHA” (Model for Airloads Reconstruction using a Trained Hybrid Architecture), was presented, which is composed of a Multilayer Perceptron and the decoder of an Autoencoder. Three compact representation models—Principal Component Analysis (PCA), Autoencoder (AE), and Variational Autoencoder (VAE)—were systematically evaluated to determine the optimal dimensionality reduction architecture; the AE was selected based on its superior reconstruction accuracy and training stability. The main feature of MARTHA is that it provides predictions of the differential pressure coefficient field in the form of monochrome images, where the pixel intensity directly represents the normalized pressure value. One of the main objectives of developing MARTHA was to create a rapid surrogate model that can approximate vortex lattice method (VLM) simulations in preliminary design and optimization tasks, particularly when thousands of wing configurations need to be evaluated. The key feature of the proposed model is its ability to predict the pressure distribution for trapezoidal wings of various geometries 10¹–10⁴ times faster than numerical models, while maintaining accuracy (R² = 0.9998). The data obtained are presented in a convenient format for their further use in CAE systems of strength analysis. To assess the practical utility of the proposed model, implementation cases were carried out using the finite element software ANSYS 18.2 for three wing configurations not present in the training dataset. The pressure fields predicted by MARTHA were mapped onto the wing meshes, and linear static structural analyses were performed. The obtained Von Mises stress distributions showed good agreement with the corresponding distributions obtained using numerical models. Full article

The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. This paper proposes a hybrid analytical–machine learning framework for efficient voltage stability assessment and classification. The proposed approach consists of two stages. First, a power flow analysis is performed to compute the Fast Voltage Stability Index (FVSI) and quantify the proximity of the system operating conditions to voltage instability. Then, the FVSI values are converted into binary stability labels to formulate a supervised classification problem. In the second stage, the Extreme Gradient Boosting (XGBoost) algorithm is employed to learn the relationship between system operating variables and the corresponding stability states. The performance of the proposed method is evaluated on the IEEE 30-bus system and compared with that of conventional machine learning and deep learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNNs). The simulation results show that the XGBoost-based framework outperforms the benchmark models in terms of classification accuracy, robustness, and computational efficiency. The proposed method provides a fast, reliable, and interpretable solution for real-time voltage stability monitoring. Therefore, it is suitable for modern smart grid applications. Full article

Ground source heat pump (GSHP) systems contribute to sustainable building decarbonization while confronting two intertwined challenges: long-term ground thermal imbalance in shallow borefields and the requirement for coordinated operation between thermal systems and electrical grid dynamics. Hybrid shallow–deep borefield configurations have been proposed to mitigate thermal imbalance for sustainable geothermal resource exploitation, yet their grid-interactive demand–response potential remains unexplored. Here, we develop a coupled thermal–electrical model for a shallow–deep mixed GSHP (SDBHE) system equipped with water-tank thermal storage, validated against scaled sand-tank experiments (3.5–8.3% error), and assess its year-round performance under time-of-use electricity tariffs for a 200,000 m² residential district in cold-climate conditions. The SDBHE system reduces the required shallow borehole count by 28% and total drilling length by 22% compared with a shallow-only baseline, saving 11% on operational electricity costs over 10 years. Integrating water-tank thermal storage with a 50% load-shifting strategy yields an additional 10.9–11% cost reduction without degrading the system’s coefficient of performance. Under higher load-shifting ratios, the combined capital and operational savings reach 19–29%, with the optimal allocation assigning the incremental high-price-period load preferentially to deep boreholes (COP 6.29 versus 5.25 for shallow). These results demonstrate that integrating shallow and deep geothermal tiers with thermal storage enables both capital-efficient borefield design and economically viable demand-side grid participation. The findings are bound by the cold-climate residential context and the rule-based control scheme; field-scale validation and lifecycle cost analysis are needed to generalize the conclusions. Full article