Search Results (1,540)

Air pollution from particulate matter (PM2.5 and PM10) poses a serious public health risk in urban environments, particularly in areas with heavy vehicular traffic. Against this backdrop, the present study proposes an Internet of Things (IoT)-based system designed to support air quality monitoring and evidence-based decision-making regarding PM2.5 and PM10 concentrations, integrating low-cost sensors with a machine learning prediction module. The study follows an experimental-applied design with a quantitative–comparative approach. Its scientific contribution is organized around an integrated IoT-ML framework addressing a concrete gap in the literature: the lack of local empirical evidence regarding which family of machine learning algorithms delivers the greatest accuracy, stability, and computational efficiency for particulate matter forecasting in mid-altitude urban environments using low-cost sensors. On one hand, the framework proposes and deploys a four-node IoT network for continuous PM2.5 and PM10 monitoring in high-traffic urban microenvironments—representing one of the first sustained deployments with low-cost, high-temporal-resolution sensors (10-minute intervals) in Arequipa, Peru. On the other hand, the study presents the most extensive benchmarking reported in the local literature: a systematic evaluation of 44 machine learning algorithms under homogeneous experimental conditions, covering classical statistical models, traditional machine learning techniques, deep learning architectures, and hybrid approaches, along with an analysis of ensemble learning strategies using Ridge stacking and K-Fold cross-validation. This unified comparative analysis—applying consistent metrics (MAE, RMSE, R², and MAPE), the same prediction horizon, and a shared dataset—provides replicable empirical evidence that had not previously been reported for the urban context of Arequipa. The results show that traditional statistical models perform poorly overall, while tree-based and boosting algorithms consistently achieve R² values above 0.90 for both pollutants. Ensemble models, particularly stacking with Ridge regression and cross-validation, yielded the strongest overall performance, demonstrating greater robustness and prediction stability. Explainability criteria were also incorporated, enabling an assessment of each base model’s individual contribution and identifying the variables most relevant to the prediction process. The methodological contribution provides future researchers with a rigorous reference framework for algorithm selection in environmental IoT systems. Taken together, the findings demonstrate that combining low-cost IoT networks with advanced machine learning and ensemble learning techniques constitutes an effective, scalable, and cost-efficient alternative for air quality monitoring, predictive analysis, and the support of informed mitigation strategies in urban environments. Full article

Polar-orbiting microwave radiometers provide indispensable all-weather measurements of the atmospheric state, yet revisit intervals of many hours leave critical gaps during rapidly evolving weather events. To address this limitation, we developed MIDAS (Microwave Inference via Diffusion Across Satellites), a probabilistic framework that estimates microwave brightness temperature (BT) fields across the geostationary full-disk domain from infrared observations at 10 min intervals. This study focuses on the five Microwave Humidity Sounder-2 (MWHS-2) humidity-sounding channels near 183 GHz, which provide vertically resolved water vapor information. MIDAS achieves relative errors below 0.5% for the majority of cases, with a channel-averaged mean absolute error of 1.15 K, outperforming a deterministic U-Net baseline (1.43 K). Beyond per-sample evaluation, MIDAS reproduces large-scale climatological patterns across the full-disk domain over a three-month summer period, consistent with Radiative Transfer for TOVS–Scattering (RTTOV-SCATT) simulations. In deep convective scenes where reconstruction is most difficult, the ensemble spread naturally tracks reconstruction difficulty, providing a built-in indicator of prediction confidence. Notably, MIDAS incorporates real-time polar-orbiting observations as physical constraints via a merge-sampling mechanism, reducing ensemble RMSE by over 20% and improving probabilistic calibration by more than 30%. Proof-of-concept assimilation experiments for two high-impact weather cases show that MIDAS-generated fields yield forecast improvements comparable to those from real satellite observations, reducing tropical cyclone track errors from approximately 110 km to 40 km and improving heavy precipitation forecasts at extreme rainfall thresholds where direct infrared assimilation shows no benefit. Overall, our framework demonstrates the potential of generative models to supplement sparse observational coverage and provide physically plausible microwave humidity fields for downstream applications. Full article

We propose and empirically evaluate a two-stage approximation to a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) for the joint bias correction of five NASA POWER reanalysis variables: minimum and maximum temperature ($T_{\min }$, $T_{\max }$), relative humidity (RH), solar radiation (Rad), and precipitation occurrence ($P_{\mathrm{bin}}$). Our primary contribution is the first operational-scale evaluation of such a framework (≈200,000 station–day observations, two orders of magnitude beyond previous studies) together with its deployment in an open-access web application. A systematic grid of more than 200 marginal configurations is evaluated on a strict chronological 70/30 hold-out (training 2016–2022; testing 2023–2025) to identify the optimal marginal specification per variable. Against a correctly specified marginal baseline, station-level linear calibration combined with the marginal GAMM removes the bulk of the systematic bias (RMSE reductions of ≈80%, 82% and 30% for $T_{\min }$, $T_{\max }$ and RH). A shared latent step, using the first principal component of the marginal residual matrix as a scalar proxy for ${\Lambda }_0\left(t\right)$, yields additional but conditional out-of-sample reductions (≈17% $T_{\max }$, 10% RH, 9% Rad; negligible for $T_{\min }$, with precipitation occurrence retained in the shared representation but its joint gain treated as exploratory); because it requires co-located donor observations, at ungauged locations the deployed pipeline applies the marginal correction only, whose spatial transfer is confirmed by leave-one-station-out cross-validation. The residual cross-correlation structure is consistent with, though not in itself proof of, Clausius–Clapeyron coupling. The trained artefacts are deployed in ColClim, an open-access R Shiny application that queries the NASA POWER API and the Open-Meteo forecast service for any location in Colombia and delivers historical bias-corrected series and short-range (1–16 day) forecasts. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

Climate change is profoundly altering precipitation and temperature patterns across high-altitude regions worldwide. The Qinghai–Tibet Plateau (QTP), known as the “Third Pole” and the “Asian Water Tower,” is among the most climate-sensitive regions and plays a critical role in the Asian water cycle, cryospheric stability, and regional ecological security. However, the complex topography and diverse climate of the QTP result in substantial discrepancies among meteorological products over this region, highlighting the necessity of a comprehensive evaluation against in situ observational records. Using records from 85 stations (1960–2022), we evaluated four products: China’s 1 km monthly dataset (CN_1km), the Climatic Research Unit gridded Time Series (CRU TS), the fifth-generation European Centre for Medium-Range Weather Forecasts land reanalysis (ERA5-Land), and TerraClimate—selected for their long-term continuity, diverse product types, and widespread regional applications. Subsequently, we compared these products with Earth System Model (ESM) simulations from the NASA Earth Exchange Global Daily Downscaled Projections based on CMIP6 (NEX–GDDP–CMIP6). This evaluation was conducted using key statistical metrics, including the coefficient of determination (R²), root mean square error (RMSE), Kling–Gupta efficiency (KGE), and bias, together with spatially distributed long-term trend analysis using the Sen’s slope estimator and Mann–Kendall test. Station-based evaluation shows that temperature datasets generally outperform precipitation datasets, with monthly mean temperature yielding R² values of 0.85–0.94, RMSE values of 2.38–4.79 °C, and KGE values ranging from −0.04 to 0.86. Monthly precipitation R² values of 0.74–0.81, RMSE values of 20.60–36.12 mm, and KGE values of 0.42–0.86. For anomalies, temperature performs better (R² = 0.41–0.67; RMSE = 0.80–1.41 °C) than precipitation (R² = 0.28–0.44; RMSE = 16.87–20.73 mm). Overall, CN_1km and TerraClimate provide the most reliable station-based temperature estimates, while TerraClimate shows the most robust precipitation performance. All four datasets consistently indicate warming and wetting trends, with temperature rising at 0.21–0.24 °C decade⁻¹ and precipitation increasing at 4.5–5.8 mm decade⁻¹, featuring stronger warming in the west and greater precipitation increases in the northeast; however, the precipitation trend in ERA5-Land does not reach statistical significance. NEX–GDDP–CMIP6 simulations reproduce comparable warming and moistening signals (0.22–0.23 °C decade⁻¹ and 4.1–4.7 mm decade⁻¹), though their precipitation distribution differs markedly from the other datasets, with the discrepancy primarily reflected in a pronounced latitudinal gradient. These results provide a reference for the selection of climate-forcing datasets in hydrological, ecological, and cryospheric studies across the QTP. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article

This paper addresses the output and employment impacts of a climate self-financed taxation/subsidy policy on CO₂ emission reduction. We model balanced climate fiscal expenditure using a two-regime CO₂-based threshold autoregressive model that separates periods of rising emissions by positive CO₂ log-differences and of falling emissions by negative CO₂ log-differences. Applied to data sets for 16 OECD countries over 23 years (1995–2018), we find that self-financing equal amounts of tax and subsidy over the lifespan of the data set yields a CO₂-reducing regime that dominates, with significant threshold and marginal policy impacts on both output and employment. The policy impacts, as shown by the panel data variance decomposition forecast, indicate that the policy shock to total output variance outweighs other effects for up to three years, and to total employment variance for up to four years. The assessment of a two-regime/threshold model of neutral fiscal policy constitutes our contribution to the literature. Full article

Forecast reconciliation has been widely studied in cross-sectional and temporal hierarchies, but its role in cross-temporal settings for photovoltaic (PV) forecasting remains insufficiently examined. In particular, the relative benefits of reconciliation across heterogeneous forecasting approaches, including statistical, machine learning, deep learning, and foundation models, have not been clearly established. This study addresses that gap by evaluating direct, univariate, and iterative cross-temporal reconciliation strategies applied to TBATS, LightGBM, KAN, NBEATSx, NHITS, and TimeGPT using Belgian PV generation data from 2020 to 2025 across weekly, daily, and hourly frequencies and national, regional, and provincial levels. Model efficacy is assessed through 52-week walk-forward cross-validation, which provides a full-year coverage. Under the fixed-configuration experimental protocol adopted in this study, the results show that the gains from reconciliation vary substantially across forecasting families. LightGBM achieved the largest observed gains, with its univariate and iterative schemes achieving global error reductions of up to 19.6% relative to the Bottom-Up benchmark. KAN, NHITS, and NBEATSx also benefited from reconciliation, with their best reconciled variants yielding reductions of up to 11.9%. TimeGPT and TBATS achieved reductions of up to 9.2% and 14.5%, respectively, although their global errors were higher than those obtained by the best machine learning and deep learning configurations in this evaluation. Across the fixed baseline configurations considered here, LightGBM obtained the lowest global errors before and after reconciliation. These findings show that cross-temporal reconciliation can be an effective post-processing strategy, but its impact depends strongly on the underlying base forecasting model. Therefore, the observed advantage of LightGBM should be interpreted as conditional on the adopted feature set, implementations, and baseline configurations. Full article

Accurate long-term monthly temperature outlooks are vital for climate risk planning in the Gulf of Mexico and Atlantic-Transition. This study addresses the gap in comparisons between classical and machine learning methods by analyzing twenty-eight years of records from 1997 to 2025 across four coastal cities. Ten modeling families were benchmarked, including the following methods: Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt–Winters (HW), Singular-Spectrum Analysis (SSA), Linear Regression (LR), Random-Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and the hybrid Forecasting Method with Filters and Residual Analysis (FMFRA). The performance was validated using Symmetric Mean Absolute Percentage Error (sMAPE), Root Mean Square Error, and Directional Accuracy within a multidimensional ranking framework. Trend analysis revealed a statistically significant warming of 0.25 °C per decade. The FMFRA framework, integrating signal filtering and adaptive residual correction, achieved the best overall performance with an optimal Mean Rank of 1.75. Non-parametric statistical validation, conducted via the Wilcoxon signed-rank test with the Holm–Bonferroni step-down correction, confirmed that FMFRA consistently outperforms most machine learning and boosting architectures. While classical methods such as HW, SSA, and SARIMA remain competitive in stable maritime climates with low volatility, FMFRA provides superior robustness in regions characterized complex thermal transitions. Overall, integrating signal filtering with residual analysis yields more stable forecasts, offering a reliable computational foundation for proactive urban energy planning and climate risk mitigation in volatile coastal environments. Full article

The expansion of China’s modernized eco-environmental monitoring networks has generated vast amounts of data. Consequently, traditional, expertise-reliant analysis is increasingly ill-suited for agile regulatory decision-making. Although large language models (LLMs) present a promising alternative, their practical deployment remains limited by domain-specific knowledge gaps, hallucinations and an inherent difficulty in managing multi-faceted ecological tasks. This study introduces EnvSentry, a novel multi-engine collaborative LLM framework designed for intelligent eco-environmental monitoring and governance. EnvSentry coordinates reasoning, instruction, and multimodal engines, supported by a dynamic, vector-indexed knowledge base and retrieval-augmented generation (RAG) to ensure factual veracity. By transitioning operational workflows from fragmented, latent batch processing to integrated, real-time intelligent agent chains, the system achieves a closed-loop capability of intent recognition, data retrieval, and quality control. The model was evaluated across distinct environmental contexts, specifically water quality anomaly detection and air quality forecasting. Results show that EnvSentry yields higher analytical precision and attribution rates than baseline methods, while compressing decision-making latency from hours to seconds. Relative to baseline models, EnvSentry achieves a 25% improvement in water quality attribution accuracy (50% to 75%), a 90% reduction in decision making latency for anomaly detection, and a 10% absolute gain in data anomaly detection accuracy. In air quality forecasting, it reduces expert judgment time from 60 to 20 min and attains >85% agreement with expert forecasts when used by non-specialist personnel. These improvements suggest a practical shift in eco-environmental monitoring—moving from fragmented, reactive measures toward an integrated and proactive system. Consequently, this approach offers a viable path toward data-driven autonomous ecological management. Full article

Functional magnetic resonance imaging (fMRI) signals exhibit complex temporal structure arising from multivariate neural dynamics, physiological variability, and measurement uncertainty. In this work, we formulate region-of-interest-level fMRI analysis as a probabilistic multi-step forecasting problem and investigate the predictability of blood-oxygen-level-dependent (BOLD) activity from an information-theoretic perspective. Using the Natural Scenes Dataset, we model multiregional BOLD activity as a stochastic process with finite memory and train multiple forecasting architectures, including linear regression, exponential smoothing, recurrent neural networks, and transformer-based models, to predict future BOLD samples from preceding temporal observations. Forecasting performance is analyzed together with entropy-based quantities, including marginal entropy, conditional entropy, and normalized predictive information measures estimated directly from model-derived predictive distributions without imposing restrictive Gaussian assumptions on the underlying BOLD dynamics. The transformer model achieved significant improvement over a naive persistence baseline ($p=0.001$) while yielding a high predictive information fraction ($\eta =75.49\%$). Post hoc directed information analysis revealed that short-horizon prediction was dominated primarily by autoregressive, within-ROI, temporal structure. Overall, the proposed framework demonstrates how probabilistic forecasting and information-theoretic analysis can be integrated to characterize the predictability, uncertainty structure, and directional organization of large-scale fMRI dynamics and may support future downstream neuroengineering and neural-state inference applications. Full article

Forecasting foreign exchange rates over long time periods depends on economic fundamentals. Short-term predictions, by contrast, depend largely on emotions, governmental announcements, the flow of capital, and media commentary. This paper proposes a suite of methods, collectively referred to as ForExAI, to predict foreign exchange rates based on time series analysis and news article analysis. The time series analysis is based on classical statistical time series techniques, such as ARIMA, as well as machine learning methods using neural networks. Separately, ForExAI uses Large Language Models to analyze news articles based on two kinds of prompts: (i) expert-written based on econometric considerations, (ii) existing prompts documented in the literature. Our findings on time series of exchange rates indicate that there are signals in the time series that can be captured even by simple methods like ARIMA(1,1,1), as well as novel machine learning methods on the time series of foreign exchange rate trades. Further, an adaptation of the Kelly criterion can increase cumulative profits. Finally, an ensemble approach often delivers slightly lower profit, but also lower volatility, leading to a higher Sharpe ratio. Regarding news article analysis, shorter prompts yield far better results than complex ones derived from expert knowledge. This work shows a method for hyperparameter tuning a collection of models that forecast complex time series, as well as the relative virtues of different versions of the Kelly criterion. The results make a pragmatic contribution as well. Because we measure profits ignoring transaction costs, our work is not directly actionable by traders, but the insights could be useful. In addition, our results point to further areas of research for traders. Full article

Climate change threatens global food security through shifts in temperature and precipitation regimes, greater frequency of extreme weather events, and cascading effects on agricultural input markets and institutional capacity. Policymakers require nationally comparable diagnostic tools that are reproducible, transparent, and grounded in open data. This study presents an interpretable machine learning framework for country-level crop yield prediction and climate sensitivity screening, using publicly available FAOSTAT-derived panel data spanning 101 countries and 10 staple crop types over 1990–2013. A gradient-boosted decision tree model (XGBoost 2.1.4) is trained on observations from 1990 to 2008 and evaluated on a strictly held-out temporal window (2009–2013), using annual mean temperature, annual precipitation, total pesticide use (as a rough proxy for agricultural input management intensity), crop type and country identifiers, and temporally lagged yield values as predictive features. The optimized model yields high predictive accuracy on held-out data (R² = 0.982; RMSE = 11,183 hg/ha; MAE = 4396 hg/ha). Ablation analysis reveals that model performance depends primarily on temporal yield persistence and crop identity, with performance declining to R² = 0.940 when lagged features are omitted, while climate-only variables explain limited variation (R² = 0.119). Notably, an ordinary least squares (OLS) baseline achieves comparable performance (R² = 0.984), suggesting that the dominant predictive signals arise from a stable temporal–cross-sectional structure rather than nonlinear modeling flexibility. SHAP-based feature attribution identifies regime-dependent temperature effects, with larger (more negative) marginal contributions under high-temperature conditions. Stylized sensitivity perturbations (+1 °C, +2 °C, −20% pesticide inputs) indicate modest mean yield changes (−1.3% to −1.6%) but substantial cross-national heterogeneity. Systematic residual analysis identifies countries exhibiting consistent over- or under-prediction patterns, offering diagnostic signals for further institutional investigation. This framework is designed as a transparent, scalable screening tool for evidence-based prioritization rather than a validated causal or forecasting instrument. It complements localized agronomic expertise and supports SDG 2 (Zero Hunger) and SDG 13 (Climate Action). Full article

Accurate district-level wheat yield forecasting is critical for Kazakhstan, the world’s seventh-largest wheat exporter. Prior remote-sensing studies typically compute vegetation indices over entire administrative units without isolating cropland, diluting the crop-specific signal and biassing remote-sensing–climate comparisons. A 25-year (2000–2024) dataset was assembled for 149 Kazakh districts (n = 2378 district–year observations, ~390 features), integrating crop-masked Sentinel-2/Landsat-7 optical indices, Sentinel-1 SAR, TerraClimate, and station, soil, and terrain data, and a HistGradientBoosting model was evaluated under both spatial (GroupKFold) and temporal (expanding-window) cross-validation. Ten-metre cropland masking substantially improved index–yield correlations, especially early in the season, and SAVI consistently outperformed NDVI from June onward. The best configuration—crop-masked optical indices with TerraClimate—achieved R² = 0.646 (RMSE = 0.349 t/ha) under spatial cross-validation, whereas adding SAR yielded no significant gain. Pre-season winter-climate data (January–March) reached about 91% of full-year accuracy, enabling forecasts months before sowing. Critically, temporal cross-validation produced a markedly lower mean R² = 0.413, a predictability gap (ΔR² = 0.233) that provides a more representative estimate of operational forecast accuracy. Residuals showed no significant spatial autocorrelation. These results indicate that cropland masking and joint reporting of spatial and temporal cross-validation are valuable for yield prediction in semi-arid continental environments. Full article

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. Full article