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11 pages, 2550 KB

Open AccessProceeding Paper

Spatiotemporal Regression and Autoregression for Fusing Satellite Precipitation Data

by Xueming Li and Guoqi Qian

Eng. Proc. 2025, 101(1), 1; https://doi.org/10.3390/engproc2025101001 - 21 Jul 2025

Viewed by 211

Most existing precipitation data fusion methods rely on reliable precipitation values, such as those observed from ground-based rain gauges, to correct the satellite precipitation estimates (SPEs) that often involve systematic biases. However, such reliable data are rarely available in many regions of the [...] Read more.

Most existing precipitation data fusion methods rely on reliable precipitation values, such as those observed from ground-based rain gauges, to correct the satellite precipitation estimates (SPEs) that often involve systematic biases. However, such reliable data are rarely available in many regions of the world, especially in rugged terrain and hostile regions, rendering the correction suboptimal. To address this limitation, we propose a novel data fusion method—Triple Collocation Spatial Autoregression under Dirichlet distribution (TCSpAR-Dirichlet)—which eliminates the need for reliable data while still having the capability to effectively capture true precipitation patterns. The key idea in our method is using the variance of the precipitation estimates at each grid location obtained from each satellite to optimally leverage the associated satellite’s weight in data fusion, then characterizing the weights on all locations by a spatial autoregression model, and finally using the fitted weights to fuse the multi-sourced SPEs at all grid locations. We apply this method to SPEs in Nepal, which does not have ground gauges in many of its mountainous areas, to collect reliable precipitation data, to produce a fused precipitation dataset with uniform spatial coverage and high measurement accuracy. Full article

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11 pages, 332 KB

Open AccessProceeding Paper

Water-Level Forecasting Based on an Ensemble Kalman Filter with a NARX Neural Network Model

by Jackson B. Renteria-Mena, Douglas Plaza and Eduardo Giraldo

Eng. Proc. 2025, 101(1), 2; https://doi.org/10.3390/engproc2025101002 - 21 Jul 2025

Viewed by 225

Abstract

It is fundamental, yet challenging, to accurately predict water levels at hydrological stations located along the banks of an open channel river due to the complex interactions between different hydraulic structures. This paper presents a novel application for short-term multivariate prediction applied to [...] Read more.

It is fundamental, yet challenging, to accurately predict water levels at hydrological stations located along the banks of an open channel river due to the complex interactions between different hydraulic structures. This paper presents a novel application for short-term multivariate prediction applied to hydrological variables based on a multivariate NARX model coupled to a nonlinear recursive Ensemble Kalman Filter (EnKF). The proposed approach is designed for two hydrological stations of the Atrato river in Colombia, where the variables, water level, water flow, and water precipitation, are correlated using a NARX model based on neural networks. The NARX model is designed to consider the complex dynamics of the hydrological variables and their corresponding cross-correlations. The short-term two-day water-level forecast is designed with a fourth-order NARX model. It is observed that the NARX model coupled with EnKF improves the robustness of the proposed approach in terms of external disturbances. Furthermore, the proposed approach is validated by subjecting the NARX–EnKF coupled model to five levels of additive white noise. The proposed approach employs metric regressions to evaluate the proposed model by means of the Root Mean Squared Error (RMSE) and the Nash–Sutcliffe model efficiency (NSE) coefficient. Full article

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20 pages, 5571 KB

Open AccessProceeding Paper

A Forecasting Method Based on a Dynamical Approach and Time Series Data for Vehicle Service Parts Demand

by Vinh Long Phan, Makoto Taniguchi and Hidenori Yabushita

Eng. Proc. 2025, 101(1), 3; https://doi.org/10.3390/engproc2025101003 - 21 Jul 2025

Viewed by 278

Abstract

In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manufacturers must ensure timely availability of these parts [...] Read more.

In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manufacturers must ensure timely availability of these parts while managing inventory efficiently. An excess of inventory can increase warehousing costs, while stock shortages can lead to supply delays. Accurate demand forecasting is essential to balance these factors, considering the changing demand characteristics over time, such as long-term trends, seasonal fluctuations, and irregular variations. This paper introduces a novel method for time series forecasting that employs Ensemble Empirical Mode Decomposition (EEMD) and Dynamic Mode Decomposition (DMD) to analyze service part demand. EEMD decomposes historical order data into multiple modes, and DMD is used to predict transitions within these modes. The proposed method demonstrated an approximately 30% reduction in forecasting error compared to comparative methods, showcasing its effectiveness in accurately predicting service parts demand across various patterns. Full article

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10 pages, 637 KB

Open AccessProceeding Paper

Improving Industrial Control System Cybersecurity with Time-Series Prediction Models

by Velizar Varbanov and Tatiana Atanasova

Eng. Proc. 2025, 101(1), 4; https://doi.org/10.3390/engproc2025101004 - 22 Jul 2025

Viewed by 390

Abstract

Traditional security detection methods struggle to identify zero-day attacks in Industrial Control Systems (ICSs), particularly within critical infrastructures (CIs) integrated with the Industrial Internet of Things (IIoT). These attacks exploit unknown vulnerabilities, leveraging the complexity of physical and digital system interconnections, making them [...] Read more.

Traditional security detection methods struggle to identify zero-day attacks in Industrial Control Systems (ICSs), particularly within critical infrastructures (CIs) integrated with the Industrial Internet of Things (IIoT). These attacks exploit unknown vulnerabilities, leveraging the complexity of physical and digital system interconnections, making them difficult to detect. The integration of legacy ICS networks with modern computing and networking technologies has expanded the attack surface, increasing susceptibility to cyber threats. Anomaly detection systems play a crucial role in safeguarding these infrastructures by identifying deviations from normal operations. This study investigates the effectiveness of deep learning-based anomaly detection models in revealing operational anomalies that could indicate potential cyber-attacks. We implemented and evaluated a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze ICS telemetry data. The CNN-LSTM model excels in identifying time-dependent anomalies and enables near real-time detection of cyber-attacks, significantly improving security monitoring capabilities for IIoT-integrated critical infrastructures. Full article

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11 pages, 493 KB

Open AccessProceeding Paper

PV Power Generation Forecasting with Fuzzy Inference Systems

by Cinthia Rodriguez, Marco Pacheco, Marley Vellasco, Manoela Kohler and Thiago Medeiros

Eng. Proc. 2025, 101(1), 5; https://doi.org/10.3390/engproc2025101005 - 23 Jul 2025

Viewed by 244

Abstract

This paper aims to implement a fuzzy system for the purpose of forecasting the output of photovoltaic (PV) systems. A bibliometric review was conducted to establish a baseline, involving the exploration of six different configuration of fuzzy systems. These systems were trained and [...] Read more.

This paper aims to implement a fuzzy system for the purpose of forecasting the output of photovoltaic (PV) systems. A bibliometric review was conducted to establish a baseline, involving the exploration of six different configuration of fuzzy systems. These systems were trained and evaluated using a sliding window technique and a validation set. The development of the study utilized data collected from 1 May 2018 to 30 June 2018 at the Universidad Autónoma de Occidente campus. The dataset was analyzed in order to identify any discernible trends, seasonal patterns, and instances of stationarity. A comparison of the six models revealed their ability to predict PV power generation, with the model with 13 lags and five fuzzy sets demonstrating results with a reasonable trade-off between training and test performance. The model achieved an R-squared value of 0.8124 and an RMSE of 29.7025 kWh in the test data, indicating that the predictions were closely aligned with the actual values. However, this suggests that the model may be overly simple or may require additional data to more accurately capture the inherent variability of the data. The paper concludes with a discussion of the model’s limitations and potential avenues for future research. Full article

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11 pages, 1161 KB

Open AccessProceeding Paper

Spatio-Temporal PM2.5 Forecasting Using Machine Learning and Low-Cost Sensors: An Urban Perspective

by Mateusz Zareba, Szymon Cogiel and Tomasz Danek

Eng. Proc. 2025, 101(1), 6; https://doi.org/10.3390/engproc2025101006 - 25 Jul 2025

Viewed by 350

Abstract

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and [...] Read more.

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest

R^{2}

(0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

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10 pages, 6510 KB

Open AccessProceeding Paper

Energy Consumption Forecasting for Renewable Energy Communities: A Case Study of Loureiro, Portugal

by Muhammad Akram, Chiara Martone, Ilenia Perugini and Emmanuele Maria Petruzziello

Eng. Proc. 2025, 101(1), 7; https://doi.org/10.3390/engproc2025101007 - 25 Jul 2025

Viewed by 1125

Abstract

Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and reducing environmental impact. Accurate forecasting of energy consumption at the [...] Read more.

Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and reducing environmental impact. Accurate forecasting of energy consumption at the community level is a key tool in this effort. Traditionally, engineering-based methods grounded in thermodynamic principles have been employed, offering high accuracy under controlled conditions. However, their reliance on exhaustive building-level data and high computational costs limits their scalability in dynamic REC settings. In contrast, Artificial Intelligence (AI)-driven methods provide flexible and scalable alternatives by learning patterns from historical consumption and environmental data. This study investigates three Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), and CatBoost, and one Deep Learning (DL) model, Convolutional Neural Network (CNN), to forecast community electricity consumption using real smart meter data and local meteorological variables. The study focuses on a REC in Loureiro, Portugal, consisting of 172 residential users from whom 16 months of 15 min interval electricity consumption data were collected. Temporal features (hour of the day, day of the week, month) were combined with lag-based usage patterns, including features representing energy consumption at the corresponding time in the previous hour and on the previous day, to enhance model accuracy by leveraging short-term dependencies and daily repetition in usage behavior. Models were evaluated using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination

R^{2}

. Among all models, CatBoost achieved the best performance, with an MSE of 0.1262, MAPE of 4.77%, and an

R^{2}

of 0.9018. These results highlight the potential of ensemble learning approaches for improving energy demand forecasting in RECs, supporting smarter energy management and contributing to energy and environmental performance. Full article

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9 pages, 423 KB

Open AccessProceeding Paper

On Errors of Signal Estimation Using Complex Singular Spectrum Analysis

by Nina Golyandina, Mikhail Senov and Alexander Khramov

Eng. Proc. 2025, 101(1), 8; https://doi.org/10.3390/engproc2025101008 - 29 Jul 2025

Viewed by 206

Abstract

Singular spectrum analysis (SSA) is a nonparametric method that can be applied to signal estimation. The extension of SSA to the complex-valued case, called CSSA, is considered. The accuracy of signal estimation using CSSA is investigated. An explicit form of the first-order errors [...] Read more.

Singular spectrum analysis (SSA) is a nonparametric method that can be applied to signal estimation. The extension of SSA to the complex-valued case, called CSSA, is considered. The accuracy of signal estimation using CSSA is investigated. An explicit form of the first-order errors is presented for the case of a constant signal and two forms of perturbations: an outlier and white noise. Also, it is shown numerically that (1) the first-order errors describe the full errors well, and (2) the formulas for accurately estimating a constant signal can be applied to a sum of complex exponentials via multiplication by the number of exponentials. Full article

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10 pages, 1977 KB

Open AccessProceeding Paper

Finite-Element and Experimental Analysis of a Slot Line Antenna for NV Quantum Sensing

by Dennis Stiegekötter, Jonas Homrighausen, Ann-Sophie Bülter, Ludwig Horsthemke, Frederik Hoffmann, Jens Pogorzelski, Peter Glösekötter and Markus Gregor

Eng. Proc. 2025, 101(1), 9; https://doi.org/10.3390/engproc2025101009 - 30 Jul 2025

Viewed by 311

Abstract

Nitrogen vacancy (NV) diamonds are promising room temperature quantum sensors. As the technology moves towards application, efficient use of energy and cost become critical for miniaturization. This work focuses on microwave-based spin control using the short-circuited end of a slot line, analyzed by [...] Read more.

Nitrogen vacancy (NV) diamonds are promising room temperature quantum sensors. As the technology moves towards application, efficient use of energy and cost become critical for miniaturization. This work focuses on microwave-based spin control using the short-circuited end of a slot line, analyzed by finite element method (FEM) for magnetic field amplitude and uniformity. A microstrip-to-slot-line converter with a

- 10

dB bandwidth of

3.2

GHz was implemented. Rabi oscillation measurements with an NV microdiamond on a glass fiber show uniform excitation over

1.5

MHz across the slot, allowing spin manipulation within the coherence time of the NV center. Full article

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10 pages, 1410 KB

Open AccessProceeding Paper

Electricity Demand Model for Climate Change Analysis in Systems with High Integration of Wind and Solar Energy

by Juanita Acosta Cortes, Marcelo Silvera, Ruben Chaer, Guillermo Flieller, Guillermo Andres Jimenez Estevez and Vanina Camacho

Eng. Proc. 2025, 101(1), 10; https://doi.org/10.3390/engproc2025101010 - 1 Aug 2025

Viewed by 249

Abstract

A novel model of the electrical demand of a power system capable of representing the hourly power load and its dependence on temperature is presented. The application of the model to the Colombian system is described with an evaluation of the error obtained. [...] Read more.

A novel model of the electrical demand of a power system capable of representing the hourly power load and its dependence on temperature is presented. The application of the model to the Colombian system is described with an evaluation of the error obtained. For the simulation of the optimal operation of systems with high renewable energy participation, a model such as the one presented is of vital importance in order to take into account the dependence of demand on meteorological variables. It is also necessary for the simulation of the expected effects of climate change on electricity demand. Full article

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8 pages, 4923 KB

Open AccessProceeding Paper

A Hardware Measurement Platform for Quantum Current Sensors

by Frederik Hoffmann, Ann-Sophie Bülter, Ludwig Horsthemke, Dennis Stiegekötter, Jens Pogorzelski, Markus Gregor and Peter Glösekötter

Eng. Proc. 2025, 101(1), 11; https://doi.org/10.3390/engproc2025101011 - 4 Aug 2025

Viewed by 365

Abstract

A concept towards current measurement in low and medium voltage power distribution networks is presented. The concentric magnetic field around the current-carrying conductor should be measured using a nitrogen-vacancy quantum magnetic field sensor. A bottleneck in current measurement systems is the readout electronics, [...] Read more.

A concept towards current measurement in low and medium voltage power distribution networks is presented. The concentric magnetic field around the current-carrying conductor should be measured using a nitrogen-vacancy quantum magnetic field sensor. A bottleneck in current measurement systems is the readout electronics, which are usually based on optically detected magnetic resonance (ODMR). The idea is to have a hardware that tracks up to four resonances simultaneously for the detection of the three-axis magnetic field components and the temperature. Normally, expensive scientific instruments are used for the measurement setup. In this work, we present an electronic device that is based on a Zynq 7010 FPGA (Red Pitaya) with an add-on board, which has been developed to control the excitation laser, the generation of the microwaves, and interfacing the photodiode, and which provides additional fast digital outputs. The T1 measurement was chosen to demonstrate the ability to read out the spin of the system. Full article

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10 pages, 3658 KB

Open AccessProceeding Paper

A Comparison Between Adam and Levenberg–Marquardt Optimizers for the Prediction of Extremes: Case Study for Flood Prediction with Artificial Neural Networks

by Julien Yise Peniel Adounkpe, Valentin Wendling, Alain Dezetter, Bruno Arfib, Guillaume Artigue, Séverin Pistre and Anne Johannet

Eng. Proc. 2025, 101(1), 12; https://doi.org/10.3390/engproc2025101012 - 31 Jul 2025

Viewed by 218

Abstract

Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of predicting extreme flash floods of a runoff-dominated hydrological [...] Read more.

Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of predicting extreme flash floods of a runoff-dominated hydrological system. A fully connected multilayer perceptron with a shallow structure was used to reduce complexity and limit overfitting. The inputs of the ANN were determined by rainfall–water level cross-correlation analysis. For each optimizer, the hyperparameters of the ANN were selected using a grid search and the cross-validation score on a novel criterion (PERS PEAK) mixing the persistency (PERS) and the quality of flood-peak restitution (PEAK). For an extreme and unseen event used as a test set, LM outperformed AD by 25% on all performance criteria. The peak water level of this event, 66% greater than that of the training set, was predicted by 92% after more training iterations were done by the LM optimizer. This shows that the ANN can predict beyond the ranges of the training set, given the right optimizer. Nevertheless, the LM training time was up to five times longer than that of AD during grid search. Full article

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8 pages, 9280 KB

Open AccessProceeding Paper

Dynamical Modeling of Floods Using Surface Water Level Time Series

by Johan S. Duque, Jorge Zapata, Lucia de Leon, Alexander Gutierrez and Leonardo Santos

Eng. Proc. 2025, 101(1), 13; https://doi.org/10.3390/engproc2025101013 - 5 Aug 2025

Viewed by 257

Abstract

We present a dynamical systems approach to modeling nonlinear flood dynamics using 20 years of water level data from Durazno, Uruguay. Flood events are identified, and their periodicity and temporal distribution are analyzed in relation to rain gauge precipitation. Phase space reconstruction enables [...] Read more.

We present a dynamical systems approach to modeling nonlinear flood dynamics using 20 years of water level data from Durazno, Uruguay. Flood events are identified, and their periodicity and temporal distribution are analyzed in relation to rain gauge precipitation. Phase space reconstruction enables data-driven neural network modeling and quantification of the relationship between water level and soil moisture. Bifurcation diagrams define basin-specific flood thresholds, offering a mechanistic framework for improved flood forecasting and risk assessment. Full article

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11 pages, 567 KB

Open AccessProceeding Paper

When Accounting for People Behavior Is Hard: Evaluation of Some Spatiotemporal Features for Electricity Load Demand Forecasting

by Guillaume Habault, Shinya Wada and Chihiro Ono

Eng. Proc. 2025, 101(1), 14; https://doi.org/10.3390/engproc2025101014 - 1 Aug 2025

Viewed by 115

Abstract

Understanding human behavior is crucial for accurately predicting Electricity Load Demand (ELD), as daily habits and routines directly influence electricity consumption patterns across temporal and spatial domains. Two approaches for representing human mobility are explored: (i) incorporating location-based Human Dynamics (HD) data, and [...] Read more.

Understanding human behavior is crucial for accurately predicting Electricity Load Demand (ELD), as daily habits and routines directly influence electricity consumption patterns across temporal and spatial domains. Two approaches for representing human mobility are explored: (i) incorporating location-based Human Dynamics (HD) data, and (ii) leveraging electricity consumption data from different contract types—Low Voltage (LV) for residential areas and High Voltage (HV) for industrial and office spaces. This study investigates which of these representations allows deep learning models to better capture the influence of human mobility on LV consumption. Focusing on mesh-level predictions, our experiments demonstrate that combining LV and HV data can reduce the spatiotemporal prediction error (STPE) of LV consumption by an average of

13.37

%. Similarly, integrating HD data with LV can achieve a

14.3

% average reduction in STPE for sufficiently large areas. While combining all three—LV, HV, and HD—can improve consistency across different areas, it does not universally lower the overall prediction error. Importantly, these experiments suggest that HV data provides more reliable results across various configurations, particularly in urban regions with strong business activity. In contrast, HD data is more effective for widespread regions characterized by significant human movement or densely populated areas. This study highlights the complementary roles of HV and HD data in improving spatiotemporal LV consumption predictions and offers valuable insights into tailoring feature selection based on area characteristics and forecasting objectives. Full article

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10 pages, 868 KB

Open AccessProceeding Paper

Multi-Class Error Detection in Industrial Screw Driving Operations Using Machine Learning

by Nikolai West and Jochen Deuse

Eng. Proc. 2025, 101(1), 15; https://doi.org/10.3390/engproc2025101015 - 7 Aug 2025

Viewed by 198

Abstract

Recent advances in Machine Learning have significantly improved anomaly detection in industrial screw driving operations. However, most existing approaches focus on binary classification of normal versus anomalous operations or employ unsupervised methods to detect novel patterns. This paper introduces a comprehensive dataset of [...] Read more.

Recent advances in Machine Learning have significantly improved anomaly detection in industrial screw driving operations. However, most existing approaches focus on binary classification of normal versus anomalous operations or employ unsupervised methods to detect novel patterns. This paper introduces a comprehensive dataset of screw driving operations encompassing 25 distinct error types and presents a multi-tiered analysis framework for error-specific classification. Our results demonstrate varying detectability across different error types and establish the feasibility of multi-class error detection in industrial settings. The complete dataset and analysis framework are made publicly available to support future research in manufacturing quality control. Full article

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8 pages, 277 KB

Open AccessProceeding Paper

Optimizing Short-Term Electrical Demand Forecasting with Deep Learning and External Influences

by Leonardo Santos Amaral, Gustavo Medeiros de Araújo and Ricardo Moraes

Eng. Proc. 2025, 101(1), 16; https://doi.org/10.3390/engproc2025101016 - 12 Aug 2025

Viewed by 217

Abstract

Short-term electrical demand forecasting is crucial for the efficient operation of modern power grids. Traditional methods often fail by neglecting system nonlinearities and external factors that influence electricity consumption. In this study, we propose an enhanced deep learning-based forecasting model that integrates external [...] Read more.

Short-term electrical demand forecasting is crucial for the efficient operation of modern power grids. Traditional methods often fail by neglecting system nonlinearities and external factors that influence electricity consumption. In this study, we propose an enhanced deep learning-based forecasting model that integrates external factors such as meteorological data and economic indicators to improve prediction accuracy. Using an ISO NE (Independent System Operator New England) dataset from 2017 to 2019, we analyze 23 independent variables to assess their impact on model performance. Our findings demonstrate that careful variable selection reduces dimensionality while maintaining forecasting accuracy, enabling the effective application of deep learning models. The CNN plus LSTM composite model achieved the lowest prediction error of 0.15%, outperforming standalone CNN (0.8%) and LSTM (1.44%) approaches. The combination of CNN’s feature extraction capabilities with LSTM’s strength in handling time series data was instrumental in achieving superior performance. Our results highlight the importance of incorporating external influences in electricity demand forecasting and suggest future directions for developing more precise and efficient models. Full article

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5 pages, 181 KB

Open AccessProceeding Paper

Forecasting Dock Door Congestion in Warehouse Logistics: An Integrated Forecast–Optimization Framework—Extended Abstract

by Vittorio Maniezzo, Livio Fenga and Giacomo Ruscelli

Eng. Proc. 2025, 101(1), 17; https://doi.org/10.3390/engproc2025101017 - 8 Aug 2025

Viewed by 125

Abstract

Dock door congestion is an essential and persistent concern within the realm of outbound warehouse logistics. The inability to accommodate outbound vehicles at the loading docks, especially during peak hours, disrupts internal warehouse operations, leads to bottlenecks, and contributes to substantial additional costs [...] Read more.

Dock door congestion is an essential and persistent concern within the realm of outbound warehouse logistics. The inability to accommodate outbound vehicles at the loading docks, especially during peak hours, disrupts internal warehouse operations, leads to bottlenecks, and contributes to substantial additional costs and delays. This paper addresses the critical issue of dock door congestion by proposing an integrated forecast–optimization framework for its prediction and management. The framework uses advanced forecasting methods and optimization techniques to increase warehouse throughput, boost operational efficiency, and predict potential congestion events using historical and real-time data. It combines two proven methodologies, maximum entropy bootstrap (MEB) and ensemble learning via bagging, with scenario-based stochastic optimization. This hybrid approach significantly improves upon traditional models by capturing the complex, non-monotonic components and multi-seasonality inherent in warehouse throughput data. Through a detailed real-world case study, we demonstrate how the proposed approach can accurately predict the number of trucks that can be serviced within specific time windows. This information is crucial for making operational decisions, such as whether to expand the warehouse. The approach can be generalized beyond the specific case study and offers valuable insights for any logistics or supply chain operation requiring the integration of stochastic optimization with predictive modeling. Full article

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10 pages, 304 KB

Open AccessProceeding Paper

A Rapid, Fully Automated Denoising Method for Time Series Utilizing Wavelet Theory

by Livio Fenga

Eng. Proc. 2025, 101(1), 18; https://doi.org/10.3390/engproc2025101018 - 25 Aug 2025

Abstract

A wavelet-based noise reduction method for time series is proposed. Traditional denoising techniques often adopt a “trial-and-error” approach, which can prove inefficient and may result in suboptimal filtering outcomes. In contrast, our method systematically selects the most suitable wavelet function from a predefined [...] Read more.

A wavelet-based noise reduction method for time series is proposed. Traditional denoising techniques often adopt a “trial-and-error” approach, which can prove inefficient and may result in suboptimal filtering outcomes. In contrast, our method systematically selects the most suitable wavelet function from a predefined set, along with its associated tuning parameters, to ensure an optimal denoising process. The denoised series produced by this approach maximizes a suitable objective function based on information-theoretic divergence. This is particularly significant in economic time series, which are frequently characterized by non-linear dynamics and erratic patterns, often influenced by measurement errors and various external disturbances. The method’s performance is evaluated using time series data derived from the Business Confidence Climate Survey, which is freely and publicly accessible via the World Wide Web through the Italian National Institute of Statistics. The results of our empirical analysis demonstrate the effectiveness of the proposed method in delivering robust filtering capabilities, adeptly distinguishing informative signals from noise, and successfully eliminating uninformative components from the time series. This capability not only enhances the clarity of the data, but also significantly improves the overall reliability of subsequent analyses, such as forecasting. Full article

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