Search Results (92)

Greenhouse agriculture in semi-arid regions faces persistent challenges from unpredictable thermal variability, frost events, and seasonal drought stress that are difficult to manage without anticipatory climate information. This paper presents the design, implementation, and validation of an end-to-end intelligent greenhouse monitoring and temperature forecasting system deployed in Donovan, San Luis Province, Argentina. The proposed platform integrates a three-layer IoT architecture with cloud-based statistical forecasting to support real-time decision making under semi-arid climatic conditions. The system integrates sensor nodes, a push-MQTT gateway co-located at the Universidad Nacional de San Luis to bypass regional API geo-restrictions, and a cloud application layer. Three forecasting strategies with a six-hour prediction horizon were evaluated: a univariate SARIMA baseline (Model 1), a SARIMAX model using four neighboring meteorological stations as individual exogenous regressors (Model 2), and a SARIMAX model employing a single correlation-weighted synthetic exogenous index (Model 3). The models were assessed using MAE, RMSE, and Diebold–Mariano statistical significance tests. The results show that directly incorporating multiple correlated exogenous variables does not improve forecast accuracy because of multicollinearity, whereas the proposed correlation-weighted synthetic index preserves the spatial predictive signal while reducing model complexity and achieving performance comparable to the baseline overall, with statistically significant improvement during the overnight block. Additional benchmarking against Random Forest, Support Vector Regression, Temporal Convolutional Networks, and Long Short-Term Memory models demonstrates that increasing model complexity does not necessarily translate into improved predictive performance for short-horizon greenhouse temperature forecasting. Together, the proposed IoT architecture and forecasting framework provide an operationally validated solution for intelligent greenhouse monitoring and predictive decision support in resource-constrained semi-arid environments. Full article

This article forecasts electricity demand over two-month, one-year, and two-year horizons using 20 years of open-access data from the COES system operator. The proposed approach applies machine learning (ML) algorithms with exogenous variables and optimized LGBMRegressor hyperparameters to reduce forecasting error. Its performance is compared with mathematical statistical models (MSMs), including SARIMAX, ARIMA, and ARIMA with cross-validation. The MSM-based approaches produced lower performance metrics than the ML-based techniques and required longer computational execution times. The implementation was carried out in Google Colab Pro using Python 3.12 and libraries such as skforecast, taking advantage of the available high RAM capacity to reduce the computational time of the two forecasting techniques analyzed. For the two-month forecasting horizon, the lowest mean absolute error (MAE) was achieved with the LGBMRegressor algorithm including exogenous variables and optimized hyperparameters, with a value of 98.69 MW, whereas ARIMA with cross-validation yielded an error of 207.93 MW. These results indicate that the use of ML algorithms for electricity demand forecasting reduces forecasting errors and requires less computational execution time. Therefore, only ML was used for the one-year and two-year forecasting horizons. Based on this result, a one-year forecast was obtained with the LGBMRegressor algorithm, yielding an MAE of 111.77 MW, while the two-year forecast produced an MAE of 96.344 MW. This work incorporated socioeconomic exogenous variables, such as quarterly GDP, population, and access to electricity, which improved the medium-term forecasting model. The resulting forecasts may be useful for both the operation and planning of the electric power system (EPS). Full article

In this technological era, electrical energy is the bloodstream for the economic and social development of any country. It is the need of the time that developing countries like Pakistan have strategic planning for efficient generation and utilisation of electricity and have as much cheap electricity as possible at their disposal while respecting environmental constraints. The overloaded and ageing infrastructure of an electrical power network can impact system reliability and the sustainability of power generation, transmission and distribution mechanisms. The initiation of the planning process depends upon accurate load estimation to optimally fulfil consumers’ power needs. This paper compares statistical, hybrid and deep learning (DL) mechanisms, including SARIMAX, SARIMA with gradient boosting (SARIMA-GB), long short-term memory (LSTM) network, STL decomposition with LSTM, and CWT-LeNet-5-LSTM, for the prediction of residential electricity demand in the LESCO region of central Pakistan. The study uses 7670 daily feeder observations recorded between 1 January 2002 and 31 December 2022. The series is modelled at its native daily resolution and partitioned chronologically into a fitting span of 5216 days, a validation span of 920 days and a test span of 1534 days beginning 20 October 2018. All models receive the same block of 14 exogenous calendar variables, four annual Fourier harmonic pairs, day-of-week and month sine and cosine terms, a weekend indicator and a linear trend, and all neural models are trained with a validation split, early stopping and restoration of the best weights rather than for a fixed number of epochs. Accuracy is assessed with MAE, RMSE, MAPE and peak normalized RMSE under one recursive protocol at forecast leads of 1, 7, 14 and 30 days, because the ranking of the frameworks depends on the lead. Averaged over three random initialisations, the proposed CWT-LeNet-5-LSTM attains the lowest error at every multi-step lead, reaching an MAPE of 2.35 ± 0.28% at lead 30 against 3.32% for the multivariate LSTM, 3.89% for SARIMA-GB and 4.47% for SARIMAX. At lead 1, SARIMA-GB is the more accurate model (0.76% against 1.20 ± 0.17%) because the previous day’s observed load dominates one-step prediction for a series whose lag-one autocorrelation is 0.978. An architecture ablation isolates the contribution of the wavelet stage, the convolutional stage, the anisotropic pooling and the calendar fusion. A stratified analysis across seasons, weekdays, weekends and high-, medium- and low-load days shows where the advantage is concentrated. Additionally, Diebold–Mariano tests identify the statistical significance of the differences. Full article

Reliable visibility forecasting is important for transportation safety and sustainable urban environmental management, particularly in the Sichuan Basin, where poor visibility remains a persistent concern. This study developed a comparative diagnostic framework for one-day-ahead prediction of continuous daily visibility in Chengdu using meteorological and air-quality observations from November 2021 to October 2024. Grey relational analysis and temporal diagnostics were used to characterize variable associations and temporal dependence. Seven methods—seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), CatBoost, long short-term memory (LSTM), Transformer, convolutional neural network–LSTM (CNN–LSTM), CNN–Transformer, and Transformer–LSTM—were evaluated under four input configurations, yielding 28 model–input combinations. SARIMAX achieved the best overall balance between predictive accuracy and generalization stability. CatBoost was the most accurate data-driven method and obtained the highest coefficient of determination (R² = 0.644) under meteorological-only inputs. Meteorological-only inputs outperformed pollutant-only and full multivariate inputs for all models. Among the deep-learning models, LSTM performed better under univariate, meteorological-only, and pollutant-only inputs, whereas Transformer benefited more from the full multivariate input. CNN preprocessing improved LSTM mainly under the full multivariate and pollutant-only configurations. The moderate maximum R² suggests that routine observations did not fully capture aerosol composition, particle number and size distributions, hygroscopic growth, aerosol–water interactions, and fog-related processes. These findings support model and input selection for daily visibility forecasting. Full article

The management of municipal solid waste (MSW) in a region driven by tourism is a challenge. The dynamics of the waste produced can fluctuate significantly. Islands are particularly vulnerable due to geographic constraints and limited infrastructure. Using data to forecast and evaluate MSW management strategies would help make policy decisions. This research introduces a data-driven framework that integrates tourism dynamics with a predictive baseline for MSW generation and facilitates ongoing evaluation of system performance. The framework comprises four operational zones, namely data acquisition, baseline modelling, deviation analysis, and dashboard-based communication. It also follows the waste stream from upstream waste generation, through midstream collection and transfer, to downstream treatment and disposal. Phuket Island, Thailand, was chosen as a case study. The island has strong tourism-driven waste variability, limited waste management infrastructure, and a clear upstream–midstream–downstream waste-management chain. Tourism was explicitly integrated as an exogenous factor to capture transient demand effects. The amount of MSW was modelled using monthly tourism data. Support Vector Regression was selected to establish the expected waste baseline owing to its high predictive accuracy (MAE = 1143 t, RMSE = 1352 t and MAPE = 3.3%) outperforming other tested models (i.e., SARIMAX, Mean Baseline, Seasonal Naive, Random Forest and Histogram-Based Gradient Boost). Then, it was used to establish the baseline under normal conditions. The residual was then calculated as the difference between the observed values, and the performance of the maintenance system can be further assessed using the deviation analysis. A normal range defined within 3$\sigma$ of the residual percentages from a baseline; values outside this range are flagged as anomalies. The results were integrated into a prototype dashboard to support operational monitoring, policy evaluation, and stakeholder-orientated interpretation. The proposed framework provides a practical basis for an evidence-based adaptive policy in tourism-dependent regions. Future work should incorporate waste composition and spatially explicit data to further improve the MSW management protocol. Considering that tourism demand exhibits seasonal patterns influenced by external factors, the results of this research offer an invaluable tool for waste management. Full article

Measuring prices in agricultural e-commerce markets is complicated by promotional spikes, rapid product turnover, and specification heterogeneity. The aim of this study is to develop and validate a reproducible framework that couples promotion-robust weekly price measurement with calibrated short-term forecasting for agricultural e-commerce input markets. Drawing on 452,489 de-identified raw transaction records (448,308 retained after quality control) of tomato, pepper, and lettuce seeds from major Chinese e-commerce platforms (July 2020–October 2025), we construct a weekly Paasche-robust price index with a Top-10 SKU basket and winsorized prices, benchmarked against Laspeyres, Fisher, Törnqvist, and GEKS-TPD formulations. A time-dummy hedonic regression is applied for quality adjustment. Short-term forecasts are generated by a two-level ensemble combining SARIMAX, Temporal Fusion Transformer, and LightGBM via inverse-MAPE weighting, with adaptive conformal intervals for uncertainty quantification. The Paasche-robust index attenuates promotional noise while co-moving closely with superlative formulas (MA-12 correlations > 0.80). Machine-learning models reduce the one-week-ahead MAPE by 42–58% relative to SARIMAX, and the weighted ensemble achieves the best four-week-ahead accuracy for two of three crops. Conformal intervals deliver near-nominal coverage at short horizons. The framework offers a reproducible, deployable tool for real-time monitoring of agricultural e-commerce input markets and supports evidence-based decisions by farmers and policymakers. Full article

In this study, daily reference evapotranspiration (ET₀) values for the period 2025–2030 for Lake Burdur, located in the Mediterranean climate zone and within the Burdur closed basin, were estimated using nested architecture focused on high accuracy. The ET₀ target corresponds to the FAO-56 Penman–Monteith reference evapotranspiration variable provided by the Open-Meteo Historical Weather API, and it is treated throughout as a standardized measure of atmospheric evaporative demand rather than as actual lake-surface evaporation or basin water loss. For this purpose, daily mean air temperature, relative humidity, shortwave surface radiation, and evapotranspiration data for the period 1984–2024 were obtained from the Open-Meteo platform. In the first stage of the study (Model 1), separate SARIMAX (statistical), XGBoost (machine learning), and LSTM (deep learning) models were applied for temperature, relative humidity, and radiation series; the model with the highest validation mean for each variable was selected. Accordingly, LSTM (Mean $R^2$ = 0.967) was determined to be the most successful model for temperature, SARIMA(X) (Mean $R^2$ = 0.812) for relative humidity, and XGBoost (Mean $R^2$ = 0.845) for the radiation variable, which is non-linear, has strong autocorrelation, and exhibits distinct seasonality. In the second stage (Model 2), these best climate predictions were used as independent variables for evapotranspiration, and LSTM provided the highest success for evapotranspiration (Mean $R^2$ = 0.941). Trend analyses revealed that the increase in temperature and evapotranspiration and the decrease in relative humidity observed in the past period will continue in the near future. The uncertainty analysis conducted using the Monte Carlo/resampling approach on historical data showed that the 95% prediction intervals largely protected the upward trend in evapotranspiration against random fluctuations. These intervals reflect residual-based uncertainty under the fitted model rather than the full predictive uncertainty of future basin evapotranspiration. The findings indicate that designing model selection appropriate to the structure of the variables within a nested prediction framework significantly improves forecast accuracy and can provide a viable decision support input for sustainable water management in Mediterranean basins experiencing water scarcity. Full article

Climate, and especially cities with hot climatic conditions, directly impact human life. In this study, hourly datasets from the central meteorological station in Diyarbakır city center for the years 1990–2022 were utilized. These data were analyzed using RayMan Pro-2.1 software, and Physiological Equivalent Temperature values were derived. The obtained Physiological Equivalent Temperature values were analyzed using the SARIMAX model implemented on a machine learning infrastructure to uncover the changes between 2022 and 2050. According to the results obtained, the Physiological Equivalent Temperature value, which was 15.42 °C in 1990 in real terms, increased by 21.3% to 18.66 °C in 2022. According to the SARIMAX model predictions, Physiological Equivalent Temperature values in 2022 are estimated to rise to 21.42 °C by 2050, reflecting an increase of 14.79%. The aim of this study is to examine the temporal variations in human bioclimatic comfort values and provide a foundation for future predictions. This will contribute to the development of urban master plans by local and administrative authorities. Full article

Accurate building performance forecasting is critical for the design and renovation of energy-saving structures, but existing methods face four key challenges: heterogeneous data fusion (sensor streams, design parameters, and environmental sequences), non-stationary physical time series, model interpretability, and sample efficiency (e.g., limited commissioning data). To address these challenges, this paper proposes Fusion-Grounded Forecasting (FGF), which is a framework integrating a gated adaptive fusion layer, deterministic trend-season decomposition, an additive predictor with component decomposition, and Bayesian regularization. This framework is designed for next-hour forecasting broadcast to hourly resolution using hourly sensor data and monthly design parameters. The dataset covers 36 months (approximately 25,920 h). In addition to the combination of existing modules, the novelty lies in the integrated architecture, in which interpretable constraints can adjust the fusion layer in both directions, with decomposition prediction alignment supporting component attributes. The framework is verified on a proprietary 36-month dataset from institutional buildings using standard prediction metrics (MAE, RMSE, MAPE, and directional accuracy) and ablation studies for comparison against 10 baselines: SARIMAX, GPR, LSTM, XGBoost, N-HiTS, Informer, Autoformer, NAM, a physics-informed hybrid, and TFT. FGF achieves a 3.1% MAPE and 92.5% directional accuracy in hourly cooling load forecasting. Ablation confirmed the contribution of each module: removing gated fusion increased the MAPE to 6.8%. Compared with manual feature engineering, the speed of the framework is increased by 1680 times, and the cost is reduced by 99.6%. The explanatory index (counterfactual reliability: 0.95; Stability of functional importance: 0.11) is in compliance with audit requirements. These results indicate that FGF connects descriptive physics with quantitative prediction. However, this study is limited to a single institutional building; transferability to residential, commercial, or industrial buildings requires further verification. While waiting for this verification, FGF has demonstrated its potential as a transparent and efficient tool to build performance models. Full article

This paper examines how value-added tax (VAT) reforms affected recorded point-of-sale (POS) spending in Saudi Arabia’s restaurant, café, and food service sector during a period of rapid payment digitalization. Two policy shocks are analyzed: the introduction of a 5% VAT in January 2018 and the increase to 15% in July 2020. Using monthly official POS data from January 2016 to January 2024, the study applies an interrupted time-series framework. Baseline estimates are obtained using Generalized Least Squares (GLS) with AR (1) correction. In contrast, seasonal SARIMAX and Error Correction Model (ECM) specifications are used as robustness checks and to distinguish short-run from long-run dynamics. Controls include food and beverage price indices, headline inflation, and COVID-19 disruptions. Results show statistically significant positive level shifts in recorded POS sales after both VAT reforms, with larger measured effects after the 2020 increase. However, the evidence suggests that these changes primarily reflect formalization of transactions, migration toward electronic payments, improved reporting compliance, and intertemporal expenditure timing rather than persistent growth in real demand. Post-reform trend coefficients indicate gradual normalization in subsequent months. ECM estimates suggest that approximately 56% of short-run disequilibrium is corrected within one month. Findings are robust across alternative specifications. The paper contributes new evidence from the Gulf region by showing that retail transaction indicators may overstate real consumption responses when tax reforms coincide with rapid financial digitalization. From a sustainability perspective, the findings highlight the role of digital financial systems and modern tax administration in improving economic transparency, strengthening fiscal sustainability, enhancing formal-sector integration, and supporting the institutional transformation objectives of Saudi Vision 2030. The results imply that fiscal-policy evaluations should jointly account for tax administration reforms and changes in payment technology. Full article

Reliable short-term hydropower forecasting is essential for dispatch planning and early electricity procurement in snowmelt-influenced power systems. This study develops a leak-free operational forecasting framework using quality-controlled hourly generation and hydro-meteorological records from eight hydropower plants in Kazakhstan. Two tasks are addressed: deterministic multi-step forecasting for D+1–D+7 and uncertainty-aware envelope forecasting for D+8–D+14 using MIN and Q90 targets. The benchmark uses Persistence as the primary baseline, against which RIDGE, SARIMAX, Random Forest, HistGradientBoosting, MLP, and LSTM are compared using Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). For D+1–D+7, the results reveal strong cross-station heterogeneity and the expected decline in skill with increasing lead time. In the aggregated comparison, SARIMAX achieves the highest mean NSE at D+1 (0.903), while RIDGE becomes strongest by D+7 (0.625), both outperforming Persistence (0.534 at D+7). At the station level, SARIMAX performs best for Kapch, Kask, Moin, Bukh, and Ustk, RIDGE is best for Shar and Lenin, and LSTM is best for Shulb. The strongest stations, Kapch and Kask, reach mean NSE values of 0.941 and 0.933, respectively, whereas Ustk and Bukh remain the most difficult cases. A central methodological contribution is a flood-sensitive switched hybrid strategy for Ust-Kamenogorsk based on an observed-generation high-flow window selected by a regime-score procedure. This strategy improves robustness at medium lead times: for SARIMAX, NSE increases from 0.587 to 0.739 at D+2 and from 0.161 to 0.559 at D+7, while for RIDGE, NSE increases from 0.549 to 0.701 at D+2 and from 0.109 to 0.435 at D+7, together with substantial RMSE and MAE reductions. For D+8–D+14, envelope forecasting remains informative, but model ranking becomes target-dependent: SARIMAX and RIDGE provide the strongest mean performance for MIN (0.664 and 0.658), whereas LSTM and RIDGE are strongest for Q90 (0.746 and 0.743). Overall, the results show that hydropower forecasting in Kazakhstan is best approached as a station-wise, regime-aware, and horizon-specific problem. Full article

This study presents the relationship between climate variables and malaria outbreaks and forecasts the future malaria incidence in Arba Minch Town and its surrounding areas. High-resolution gridded climate data (~4 km × 4 km) covering the period 1981 to 2020 was obtained from the Ethiopian Meteorological Institute. Additionally, Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) were used to analyze future climate patterns. Malaria case data were obtained from local health centers located in Arba Minch town and surrounding woredas. Malaria projections were simulated using the Seasonal Autoregressive Integrated Moving Average (SARIMAX) model. Climate projections indicate a significant rise in mean temperature by the end of 21st century, increasing by 2.9 °C under SSP2-4.5 and 3.48 °C under SSP5-8.5. Average monthly rainfall during the baseline period (70.53 mm) is expected to increase to 94.18 mm and 86.09 mm under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. Malaria case distribution during the baseline period (2005–2017) ranged from 79 to 552 cases per month, while future projections suggest that cases will increase by approximately 600 in the near-term and up to more than 1000 cases by the end of the century. The SARIMAX model effectively captured seasonal variations and short-term fluctuations demonstrating a strong forecasting performance. The model generally indicated that wetter conditions and moderate temperatures will favor mosquito breeding and intensify malaria transmission. Full article

The transition towards data-driven energy management requires predictive frameworks capable of handling the nonlinear and non-stationary nature of natural gas consumption. Traditional static models often struggle to adapt to rapid regime shifts in liberalized markets. To address this forecasting problem, this study proposes a convex ensemble weight optimization framework. Moving beyond simple model averaging, we formulate the ensemble weighting problem as a constrained convex optimization task on the unit simplex. We utilize the Frank–Wolfe algorithm (Conditional Gradient) to dynamically optimize the weights of a heterogeneous set of base learners, including SARIMAX, XGBoost, N-HiTS, and Temporal Fusion Transformers (TFTs). Our results on the Czech gas market dataset demonstrate that this mathematically grounded approach achieves a Mean Absolute Percentage Error (MAPE) of 4.25%, which compares favorably to individual models such as N-HiTS (5.31%) and static averaging (6.74%). While the accuracy gain over greedy ensemble selection is marginal, the proposed convex formulation offers improved stability and interpretability, which are practical advantages for operational deployment. Full article

The rise of shared e-bike systems presents a promising solution for sustainable urban mobility, yet their operational efficiency is often hampered by unpredictable user demands. This challenge directly impacts the achievement of SDG 11 by creating service inconsistencies that can deter users. To address this, we propose a data-driven methodology for optimizing resource allocation in shared e-bike systems. Based on large-scale trip data from Ningbo, China, our analysis reveals significant spatiotemporal demand regularities at a fine-grained, cell-based level, including pronounced commuting peaks and clear spatial heterogeneity between high- and low-demand zones. Building upon these findings, we implement a SARIMAX model to generate accurate, hourly, day-ahead demand forecasts that incorporate key contextual information. Our results indicate that the SARIMAX model provides substantial improvements in predictive accuracy while offering superior interpretability and practical computational efficiency. The resulting forecasts enable data-informed decision-making for critical operations such as fleet rebalancing, battery swapping, and parking zone management. This study provides a robust and routine transparent tool for shared mobility operators, demonstrating how industrial engineering principles and statistical modeling can directly enhance the sustainability and user experience of urban transportation systems. Full article

Considering the growing potential of artificial intelligence (AI), its application has become increasingly relevant in climate-related studies and energy assessments. In this study, the Random Forest algorithm was applied to impute missing values in time series of air temperature, wind speed, atmospheric pressure, and wind direction. The performance of the data imputation was evaluated using RMSE, MSE, and MAE metrics, as well as the Kolmogorov–Smirnov (KS) test, which supported the selection of the most appropriate exogenous variable. Subsequently, short-term wind speed forecasting was performed using the SARIMAX model, and monthly energy generation was estimated for the V80/2000, SWT-2.3-101, and S95/2100 wind turbine models. The proposed methodology was applied to data from 50 conventional meteorological stations of the National Institute of Meteorology (INMET) located in Northeast Brazil. The results indicate that the gap-filling procedure was effective, particularly for wind speed and mean air temperature. Moreover, the SARIMAX model demonstrated good forecasting performance at most of the analyzed stations. Overall, the findings suggest that the majority of the locations analyzed present favorable conditions for wind-based electricity generation. Full article