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The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape, digitising the operations and facilities in a supply chain network is essential to make the system autonomous and develop strategies for enhancing resilience, transparency, and efficiency. The COVID-19 pandemic highlights the necessity of sustainable solutions for the hybrid mode of operations. To overcome several challenges, including price optimisation, demand forecasting under uncertainty, supply-demand gap reduction, take into account vulnerability, competitive business environment and risk, the supply chain needs to be streamlined with technology-driven infrastructures incorporating physical and information flow into overall supply chain processes. The digitisation aspect encompasses adopting cutting-edge technologies such as enterprise resource planning (ERP) for supply chain visibility, e-hailing platforms, real-time data analytics, the Internet of Things (IoT) and Internet of Behaviour (IoB), blockchain-driven technology, as well as additive manufacturing, enabling seamless connectivity and communication among diverse stakeholders. This revolution enables strategic integration of various entities and state of the art data-driven decision-making, providing real-time insights into logistics movements, demand forecasting, production planning and inventory levels. Supply chain digitisation and management emphasises collaboration with supply chain partners to identify important factors, optimise costs and enhance overall supply chain resilience. Digitisation and management are technological evolutions and strategic shifts integrating analytical tools, allowing businesses to formulate models to improve performance. The implementation of blockchain-driven technology solidifies trust and safety transactions by creating an immutable and transparent log, mitigating threats and enhancing traceability. Digitisation and management exemplify a transformative journey towards a more connected, data-driven, and agile global supply chain ecosystem. [ABSTRACT FROM AUTHOR]

Forecasting for intermittent demand is considered a difficult task and becomes even more challenging in the presence of obsolescence. Traditionally the problem has been dealt with modifications in the conventional parametric methods such as Croston. However, these methods are generally applied at the observed frequency, ignoring any additional information, such as trend that becomes prominent at higher levels of aggregation. We evaluate established Temporal Aggregation (TA) methods: ADIDA, Forecast Combination, and Temporal Hierarchies in the said context. We further employ restricted least-squares estimation and propose two new combination approaches tailored to decreasing demand scenarios. Finally, we test our propositions on both simulated and real datasets. Our empirical findings support the use of variable forecast combination weights to improve TA's performance in intermittent demand items with a risk of obsolescence. [ABSTRACT FROM AUTHOR]

Forecasts of demands or prices become increasingly unreliable as the future becomes more distant. It is, therefore, beneficial to show that optimal decisions during an initial time interval are either partially or wholly independent of the forecasted data from some future time onwards. Using a commodity trading model as an example, we obtain conditions that allow us to make optimal buying and selling decisions for a commodity in some initial time interval without knowing its price forecast beyond some future time. Such an initial time interval is called a decision horizon and the time up to which the forecasted data is required to make the optimal decisions during the decision horizon is called a forecast horizon. We use the maximum principle to solve the example and show that the decision and forecast horizons in the problem arise from lower and upper bounds imposed on the on-hand inventory of the commodity. [ABSTRACT FROM AUTHOR]

Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study examines the empirical outcomes of some existing forecast combination methods and proposes a generalised feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions. [ABSTRACT FROM AUTHOR]

With the explosion of e-commerce, optimising parcel transportation has become increasingly important. We study the long-haul stage of parcel transportation which takes place between sorting centres and delivery depots and is performed on a two-level hierarchical network. In our case study, we describe the application framework of this industrial problem faced by a French postal company: There are two vehicle types that must be balanced over the network on a daily basis, and there are two possible sorting points for each parcel, which allows a better consolidation of parcels. These industrial constraints are formalised in the Long-Haul Parcel Transportation Problem (LHPTP). We present a Mixed Integer Linear Program (MILP) and a hierarchical algorithm with aggregation of demands which uses the MILP as a subroutine. We perform numerical experiments on large-size datasets provided by a postal company, which consist of approximately 2500 demands on a network of 225 sites. These tests enable the tuning of certain parameters resulting in a tailored heuristic for the LHPTP. Our algorithm can serve as a decision aid tool for transportation managers to build daily transportation plans, modeled on solutions produced given daily demand forecasts and can also be used to improve the network design. [ABSTRACT FROM AUTHOR]

Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focussing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies. [ABSTRACT FROM AUTHOR]

Efficient resource management methods are essential for spare parts used in the maintenance and repair of equipment. Forecasting plays a critical role in planning, especially under demand uncertainty. Existing works regarding spare parts with intermittent demand focus on the mere forecasting model while integrating the planning and forecasting models are not sufficiently investigated. We examine the interaction between two models to optimise planning and forecasting decisions and prevent sub-optimality. This paper presents two mathematical models, including a planning model that determines stock level, spare part order assignment to suppliers, equipment repair assignment, and the number of intervals over the planning horizon. The second model is the forecasting model by Support Vector Machine (SVM). Considering uncertainty, demand estimation is performed by piecewise linearisation considering the optimal number of intervals in the planning model used in forecasting. An interactive procedure is developed to optimise models. We use an empirical investigation from an oil company providing the spare part supply chain data. The analyses show that demand estimation by piecewise method and integrating the decisions optimises the cost, improves the forecasting accuracy, and planning performance. Moreover, we offer several insights to practitioners that shed light on spare part planning and forecasting decisions. [ABSTRACT FROM AUTHOR]

This study investigates the role of human intervention in artificial intelligence/machine learning (AIML)-driven predictions. By doing so, we distinguish between three different types of human-AIML collaboration: automation, adjustable automation, and augmentation. We theorize that prediction uncertainty and time horizon represent two critical determinants of forecast accuracy. Based on a field experiment involving AIML-driven demand forecasts approximately 1,888 stock-keeping units in the retail industry, we rely on a multivalued treatment effect methodology to measure the effects of human-AIML collaboration on forecast accuracy. Our findings show that human intervention complements AIML-driven forecasts most effectively (augmentation) in predictions with long time horizons and low uncertainty. However human intervention is least likely to contribute to the effectiveness of AIML predictions (automation) in environments with short time horizons and high uncertainty. We discuss implications for extant theory and propose a framework outlining the conditions in which human intervention is most likely to add predictive value to human-AIML collaborations. [ABSTRACT FROM AUTHOR]

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation–Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future. [ABSTRACT FROM AUTHOR]

Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This study seeks to create an application capable of predicting procedure codes by analysing clinicians' operative notes, aiming to streamline their workflow and enhance efficiency. We downstreamed an existing and a native German medical BERT model in a secondary use scenario, utilizing already coded surgery notes to model the coding procedure as a multi-label classification task. In comparison to the transformer-based architecture, we were levering the non-contextual model fastText, a convolutional neural network, a support vector machine and logistic regression for a comparative analysis of possible coding performance. About 350,000 notes were used for model adaption. By considering the top five suggested procedure codes from medBERT.de, surgeryBERT.at, fastText, a convolutional neural network, a support vector machine and a logistic regression, the mean average precision achieved was 0.880, 0.867, 0.870, 0.851, 0.870 and 0.805 respectively. Support vector machines performed better for surgery reports with a sequence length greater than 512, achieving a mean average precision of 0.872 in comparison to 0.840 for fastText, 0.837 for medBERT.de and 0.820 for surgeryBERT.at. A prototypical front-end application for coding support was additionally implemented. The problem of predicting procedure codes from a given operative report can be successfully modelled as a multi-label classification task, with a promising performance. Support vector machines as a classical machine learning method outperformed the non-contextual fastText approach. FastText with less demanding hardware resources has reached a similar performance to BERT-based models and has shown to be more suitable for explaining the predictions efficiently. [ABSTRACT FROM AUTHOR]

Taking the bike‐sharing travel demand (BSTD) as an example, this study investigates the potential of Nighttime Light (NTL) data to optimize forecasting performance and replace the land use factors. Stepwise regression is trained with the travel demand in each unit as the dependent variable, and land use factors are introduced as the independent variable one by one, which finds the set of independent variables. Five machine learning algorithms driven by ensemble learning and decision trees including the GBDT, Random Forecast, Adaboost, Extratrees, and Catboost, are employed and evaluated to achieve comparative analysis of "before considering‐after considering NTL data". The methodological verification of Beijing city shows: (1) Adaboost and GBDT are superior to all other algorithms, since they generally have the highest R2, lowest RMSE, and lowest absolute MAPE. (2) All methods by employing NTL data obviously optimize the performance of BSTD forecast with decreased RMSE, decreased MAPE, etc. In particular, GBDT performs the best in reducing MSE, with a percentage of −99.99% in the training set and −86.985% in the test set, which AdaBoost, Extratrees, and Catboost follow. (3) Land use factors no longer make sense in predicting BSTD after employing NTL data, and NTL data has covered the roles of land use factors to ensure accuracy. The conclusions presented here enrich our understanding of the relative roles of land use factors and NTL data in travel demand and boost our optimization in traffic prediction in the future. [ABSTRACT FROM AUTHOR]

Accurate prediction of future urban land demand is essential for effective urban management and planning. However, existing studies often focus on predicting total demand within an administrative region, neglecting the spatiotemporal heterogeneities and interrelationships within its subregions, such as grids. This study introduces a dynamic spatiotemporal rolling prediction model (STRM) that integrates historical trends, neighborhood status, and spatial proximity for spatially explicit prediction of urban land demand at a grid level within an administrative region. STRM leverages historical urban land demand and proximity information from neighborhood grids to predict future demand of the foci grid. By integrating history and neighborhood information into a deep forest model, STRM provides an approach for rolling predictions of grid‐level urban land demand. Parameter sensitivity and structural sensitivity analyses of STRM reveal the impact of historical lags, neighborhood size, and spatial proximity on urban land demand predictions. Application of STRM in Wuhan demonstrated the performance of STRM over a 17‐year period (2000–2017), with an average adjusted R2 of 0.89, outperforming other urban land demand prediction models. By predicting demand on a year‐by‐year basis, STRM effectively captures spatiotemporal heterogeneity and enhances the resolution of urban land demand prediction. STRM represents a shift from static macroscopic to dynamic microscopic prediction of urban land demand, offering valuable insights for future urban development and planning decisions. [ABSTRACT FROM AUTHOR]

The use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation. [ABSTRACT FROM AUTHOR]

Developing an accurate tourism forecasting decision support system can help the tourism department achieve optimal resource allocation, which is crucial for achieving sustainable tourism operation management and a circular economy. Recent decades have witnessed the frequent strikes of crisis events and mega-events, which profoundly influence tourist arrival volume and bring a great challenge to forecasting tourist arrival volume. To solve this issue, we develop a deep learning framework to forecast the tourist arrival volume utilizing search engine data containing the trends of tourism intention and different event information. Our proposed model is novel for the following reasons: (1) The disturbance value can predict tourist arrival volume in coordination with the trend of travel plans. (2) Compared with the traditional models, our model can reduce the complexity of the model while maintaining accuracy. (3) Our proposed framework introducing event-related search volumes can capture the concerns of tourists and the potential loss of tourist arrivals, enhancing the model's predictive power. Experimental results show that our model can accurately forecast the tourist arrival volume by employing the monthly data in Beijing and Sanya, China. Moreover, our findings provide policymakers with more understanding of the relationship between various predictive factors and tourist arrivals. Based on the forecasting results, allocating an appropriate amount of clean energy transportation capacity, garbage treatment capacity, and fresh food supply capacity to the city can effectively promote the circular economy. [ABSTRACT FROM AUTHOR]

An urban water demand with high accuracy and reliability plays fundamental role in creating a predictive water supply system. This is necessary to implement mechanisms and systems that can be used to estimate water demands. The paper aims to forecast the amounts of clean water given to metropolis with single multiplicative neuron model artificial neural network. For this purpose, a regular data set of Istanbul metropolis was selected as a model and utilised in the application process. Single multiplicative neuron model artificial neural network is a neural network that does not have the hidden layer unit number problem that many shallow and deep artificial neural networks have. In this study, the black hole optimization algorithm is used for the first time in the literature for the training of the single multiplicative neuron model artificial neural network. In line with the analysis results, it is concluded that the proposed new approach achieved better prediction results than the other compared methods for the time series of amounts of clean water given to metropolis. [ABSTRACT FROM AUTHOR]

Electric shared mobility is flourishing in urban transportation. However, the problem of uneven vehicle distribution and untimely vehicle charging hampers user trip experience and system operation efficiency. To overcome these challenges, this study proposed a multi-phase vehicle relocation optimization approach for one-way station-based carsharing systems. In phase one, a micro-level shared travel demand forecasting model was developed to capture the number of orders in the short-term future. In phase two, stations were divided into different categories based on the results of user travel demand forecast. In phase three, the minimization of driving mileage and carbon emissions was taken as the optimization objective, and a solution method combining Gurobi solver and charging priority ranking was designed. Finally, the effectiveness and advantages of the proposed model and algorithm were comprehensively validated through a case study using real passenger orders and geographic data from the city of Shanghai, China. [ABSTRACT FROM AUTHOR]

Due to the limitations of existing tourism demand forecasting models, data with frequencies lower than those of the tourism demand need to be processed in advance and cannot be directly used in a model, which leads to the loss of timeliness and accuracy in tourism demand forecasting. Taking the inbound tourism of the United States prior to and during the COVID-19 pandemic as an example, this study systematically examines the impact of data frequency processing on tourism demand modeling and forecasting, through the construction and comparison of three categories of models, with a particular focus on the first developed multiple mixed-frequency specification of reverse mixed-data sampling (RMIDAS) model. The results confirm the positive effect of multiple mixed-frequency models, which can directly use various original data frequencies, in improving the accuracy of tourism demand forecasting. This study also provides important guidance for future research on high-frequency tourism variables forecasting. [ABSTRACT FROM AUTHOR]

Studies on probabilistic demand forecasting remain relatively limited compared with point forecasting, despite it being especially valuable for operational and planning purposes. This paper demonstrates different applications of probabilistic forecasting in demand response, together with Physical-Based Load Models. The first application shows how to determine the percentage of uncertainty potentially covered by demand response throughout a day, whereas the second application deals with obtaining the number of consumers that would be needed in demand response actions for a desired percentage of coverage. Finally, in the third application, a specific hourly strategy is proposed for demand response policies. These applications facilitate the aggregator's energy purchasing in the market and the planning of subsequent consumption adjustments through demand response to minimize deviations from the purchased energy. The approach is illustrated using hourly demand data from a small Spanish city, and two machine learning methods were used to produce a set of probabilistic forecasts and compare results: Linear Quantile Regression and a Quantile Regression Forest. Main results show a significant reduction in deviations from the purchased energy after applying the proposed strategy, especially in the case of the forecasting method being less accurate (40.95% and 33.62% of reduction, respectively). [ABSTRACT FROM AUTHOR]

Globalization has contributed to the increasing complexity of supply chain structures. In this regard, precise demand forecasting for the intricate supply chain holds paramount importance in effective supply chain management. Traditional statistical models and deep learning methods often face challenges in efficiently discerning correlations within a myriad of interconnected demands. To tackle this issue, this paper proposes an intricate supply chain demand forecasting method based on graph convolution networks adept at handling non-Euclidean data. First, the companies within the supply chain are treated as nodes in the graph structure, and the relationships between them are treated as edges, with demand data serving as the features of these edges. Then, a graph convolutional network is constructed to aggregate node and edge information. Through a multi-layer network, the relationships among nodes, edges, and historical demand are established to facilitate the prediction of supply chain demands. In this process, the graph convolutional network incorporates supply chain connectivity information into demand time series analysis. This integration of surface-level topological features and deeper latent correlation attributes across the supply chain's nodes refines the demand forecasting precision across the entire supply chain. The validation experiment in this paper is grounded in sales data of a singular product from multiple sales nodes of an electronics company. The results demonstrate that the proposed method surpasses four other traditional demand forecasting algorithms significantly in terms of accuracy, providing substantial evidence for the superior performance of graph networks in the analysis of intricate supply chain relationships. [ABSTRACT FROM AUTHOR]

This study delves into the transformative potential of integrating big data analysis and information sharing in innovation and entrepreneurship education. Employing a comprehensive methodology encompassing K-Means Clustering, Decision Trees, Apriori Algorithm, and Neural Networks, the research investigates student engagement patterns, influential factors, collaborative relationships, and predictive modelling within educational settings. The findings reveal significant outcomes, with K-Means achieving a clustering precision of 75%, Decision Trees demonstrating an accuracy of 82%, Apriori Algorithm uncovering frequent itemsets with 68% support, and Neural Networks achieving a notable accuracy of 90%. Drawing insights from a diverse range of literature, including studies on big data management, demand prediction models, ecological approaches to entrepreneurship education, qualitative inquiries into startup strategies, applications of ICTs in education, and the impact of virtual gaming on SMEs' growth, the research provides a robust foundation for understanding innovation and entrepreneurship education. This study contributes to both theoretical understanding and practical implications, guiding educators and policymakers in tailoring interventions and strategies to foster an adaptive and effective educational environment.. [ABSTRACT FROM AUTHOR]

This research introduces a groundbreaking approach to supply chain optimization and management, termed as Deep Reinforcement Learning based Supply Chain Optimization and Management (DRL-SCOM). At the core of this approach is the utilization of advancements in Deep Reinforcement Learning (DRL), specifically through the integration of Randomized Ensembled Double Q-learning (REDQ) and Trust Region Policy Optimization (TRPO). DRL-SCOM is designed to effectively tackle the inherent complexities and dynamic challenges that are characteristic of supply chain management. One of the key strengths of DRL-SCOM lies in its use of REDQ, which plays a crucial role in mitigating the overestimation bias commonly associated with traditional Q-learning methods. This results in more accurate value estimation and policy improvement, a critical factor in the effective management of supply chains. Additionally, the integration of TRPO into the framework brings the advantage of safe and stable policy updates. Such stability is vital for maintaining the robustness required in the fluctuating environment of supply chain operations. The combination of REDQ and TRPO in DRL-SCOM creates a powerful synergy. REDQ's ensembled learning approach, when fused with TRPO's trust-region method, enables the framework to efficiently navigate the complex and high-dimensional decision space typical of supply chains. This allows for real-time optimization of decisions while staying within operational constraints. The DRL-SCOM methodology shows significant potential in addressing various aspects of supply chain management, from demand forecasting and inventory management to logistics, adeptly handling the nonlinearities and uncertainties that are prevalent in these areas. Thus, the DRL-SCOM framework emerges as an innovative solution, pushing the frontiers of traditional supply chain management. It paves the way for a more agile, responsive, and intelligent system, equipped to adapt to changing market demands and operational challenges. This approach represents a significant stride towards transforming supply chain management into a more advanced, data-driven, and adaptive field. [ABSTRACT FROM AUTHOR]

An accurate forecast of current and future demand is an essential initial step for almost all the facets of supply chain optimization, including inventory strategy, production scheduling, distribution management, and marketing policies. Simply put, a more accurate demand prediction can lead to a more optimized supply chain process, allowing for better inventory control and higher customer satisfaction. Classical demand predictions are based principally on qualitative approaches relying on data from experts' opinions; quantitative forecasts based on historical data through statistical and artificial neural network models or a mix of qualitative and quantitative techniques that is also widely used and has shown good performances. Detergents and cleaning products demand is extremely volatile and has undergone substantial change, especially during the COVID-19 health crisis. In this paper, we present a hybrid Neural Network approach for accurate demand forecasts of the detergent manufacturing industry. It mainly consists of the combination of Long Short-Term Memory (LSTM) with Convolution Neural Network (CNN) based approaches. We performed a series of experiments on real data sets and assessed the performance of the proposed CNN-LSTM hybrid model. Numerical results showed that the combination of LSTM layers with complementary CNN layers provides more accurate results than other state-of-the-art forecasting models. [ABSTRACT FROM AUTHOR]

Accurate prior information of passenger flow demand on high‐speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high‐speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in‐depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high‐speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real‐world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data‐driven guidance for resource allocation and make scientific decisions in the railway industry. [ABSTRACT FROM AUTHOR]

Customer maintenance is of vital importance to the enterprise management. Valuable assessment and efficient prediction for customer ordering behavior can offer better decision-making and reduce business costs significantly. According to existing studies about customer behavior regularity segment and demand prediction most focus on e-commerce and other fields with large amount of data, making them not suitable for small enterprises and data features like sparsity and outliers are not mined when doing regularity quantification. Additionally, more and more complex network structures for demand prediction are proposed, which builds on the assumption that all the samples have predictive value, ignoring the fine-grained analysis of different time series regularity with high cost. To deal with the above issues, a multi-step regularity assessment and joint prediction system for ordering time series is proposed. For extracting features, comprehensive assessment of customer regularity based on entropy weight method with the result of predictability quantification using K-Means clustering algorithm, real entropy, LZW algorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to 'how high the regularity of customers is', filling the gap in the field of regularity quantification, but also provides a theoretical basis for demand prediction models selection. Prediction models: Random Forest regression, XGBoost, CNN and LSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of the proposed regularity quantitation method. Moreover, a merged CNN-BiLSTM neural network model is established for predicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms, which performs better on the data set compared to others. Random Forest is still used for prediction of customers with high regularity due to its high training efficiency. Finally, the results of prediction, regularity quantification, and classification are output from the intelligent system, which is capable of providing scientific basis for corporate strategy decision and has highly extendibility in other enterprises and fields for follow-up research. [ABSTRACT FROM AUTHOR]

Science foremost derives from our curiosity about the world. Can we make sense of the phenomena we see around us? Given that understanding, can we predict previously unimagined phenomena? How do things work? Can we use what we discover to invent new technologies? One class of questions that has mesmerized observers, dating at least to early cave paintings of hunters and their prey, surrounds the nature of the phenomenon we refer to as life. Over the centuries, scientists have found a broad array of surprisingly different techniques for observing, measuring, characterizing and explaining the living world. Microscopes provide a dazzling view of a previously unseen reality that tells us how living organisms are made up and how their components are organized and move. The tools of molecular science tell us the sequence and structure of the macromolecules that fill cells. The data explosion that has attended the development of a new generation of high-throughput tools for querying the living world demands that we have some way of accounting for those data that both provide intuition and make dangerous predictions with no after-the-fact parametric wiggle room. In this special issue of Current Biology , leading researchers explore how physical approaches have contributed to various fields of biology. Here, to introduce this special issue, I consider some of the ways in which viewing the living through a physical lens allows us to see things that might otherwise remain hidden. In this Guest editorial for the special issue, Rob Phillips considers some of the ways in which viewing the living through a physical lens allows us to see things that might otherwise remain hidden. [ABSTRACT FROM AUTHOR]

With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply–demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs. [ABSTRACT FROM AUTHOR]

Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks. [ABSTRACT FROM AUTHOR]

Accurate traffic volume data are crucial for effective traffic management, infrastructure development, and demand forecasting. This study addresses the challenges associated with traffic volume data collection, including, notably, equipment malfunctions that often result in missing data and inadequate anomaly detection. We have developed a deep-learning-based model to improve the reliability of predictions for annual average daily traffic volume. Utilizing a decade of traffic survey data (2010–2020) from the Korea Institute of Civil Engineering and Building Technology, we constructed a univariate time series prediction model across three consecutive sections. This model incorporates both raw and adjusted traffic volume data from 2017 to 2019, employing long short-term memory (LSTM) techniques to manage data discontinuities. A power function was integrated to simulate various error correction scenarios, thus enhancing the model's resilience to prediction inaccuracies. The performance of the model was evaluated using certain metrics, such as the mean absolute error, the root mean squared error, and the coefficient of determination, thus validating the effectiveness of the deep learning approach in refining traffic volume estimations. [ABSTRACT FROM AUTHOR]

The generation of renewable energy encounters numerous obstacles, chiefly the unpredictability of renewable sources. When new energy generation prediction is lower than expected, it needs to be supplemented by other energy sources, which may lead to instability in the power grid if the deviation is large. When the prediction of new energy generation exceeds expectations, it will lead to energy waste. To address these issues, this paper proposes a Deep Neural Network-based fusion framework, which can improve the prediction accuracy of new energy and achieve a low-carbon, economical, and stable power grid. Within this structure, feature engineering is conducted initially. Subsequently, a combination of traditional tree algorithms like the Gradient Boosting Decision Tree, linear approaches such as the Least Squares Method, and nonlinear neural networks, for instance, Recurrent Neural Networks, are employed for individual model regression purposes. In the final step, both the original time-series data and the outcomes from the individual models are integrated into a deep neural network to derive the ultimate forecasting outcomes. By using our method, the electricity cost has been reduced by 26.5% and the carbon emissions have been decreased by 14.2%. Experiments have been carried out using actual community data, confirming the effectiveness of the proposed approach. The findings indicate that the integration of DNN with traditional and modern machine learning techniques can significantly improve the forecasting of renewable energy generation. This advancement contributes to the creation of a more sustainable, economical, and stable power grid. [ABSTRACT FROM AUTHOR]

This study regarding the strategic outlook for human resource needs in India's agriculture and horticulture sectors for the next 20 years addresses the evolving economy's competitive skill requirements. Supply projections were made considering factors like historical growth, seat availability, graduation delays, placement rates, replacement needs, adjusted stock, etc. Demand projections were made considering historical growth of the sector, attrition rate, vacancies, contractual employment, new job roles, etc. In 2020, the available stock of agriculture graduates was 301,295 against a demand of 414,592, and horticulture graduates were 34,300 against a demand of 119,635. By 2040, the supply-demand gap is expected to narrow to 8% for agriculture and 21% for horticulture. The study's insights will help shape higher education, support evidence-based policy-making, and assist in implementing the National Education Policy 2020. [ABSTRACT FROM AUTHOR]

The study aims to analyze demand forecasting methods in an animal feed factory located in Minaçu, Goiás, with the goal of identifying an efficient predictive model to optimize inventory management and enhance the company's competitiveness. The methodology adopted was descriptive and applied, utilizing bibliographic, documentary, and field research, with both primary and secondary data, applying quantitative analysis for interpreting the results. Various forecasting methods were tested, such as Simple Moving Average, Simple and Double Exponential Smoothing, and Linear Trend, comparing their performance based on metrics like Mean Absolute Deviation (MAD) and Mean Squared Error (MSE). The results indicated that the Simple Moving Average performed best for forecasts in stable and nonseasonal environments, while Simple Exponential Smoothing was effective in contexts where recent changes carry more weight. The analysis concluded that implementing an appropriate demand forecasting model can reduce operational costs and improve the company's ability to adjust production to market fluctuations, thereby increasing its competitiveness. Additionally, the use of hybrid techniques combining different forecasting methods was suggested to increase the accuracy of forecasts and the efficiency of inventory management. [ABSTRACT FROM AUTHOR]

The high demand for generation and development in wind electrical power competitiveness has gained significant popularity in wind energy and speed forecasting models. It is also an essential method for planning the wind power plant system. Several models were created in the past to forest the speed and energy of the Wind. However, results have very low prediction accuracy due to their nonlinear and irregular characteristics. Therefore, a novel Modular Red Deer Neural System (MRDNS) was developed in this research to forecast wind speed and energy effectively. Primarily, the system accepted the data from the wind turbine SCADA database and preprocessed it to remove the training flaws. Further, the relevant features are extracted. The complexity of the prediction process was reduced by processing the relevant features. By analysing these features, the wind speed and energy were predicted in accordance with the fitness function of the MRDNS. The model obtained higher prediction accuracy. The recommended strategy was checked in the Python platform and the robustness metrics including RMSE, MSE, and precision were computed. The model scored 99.99% prediction accuracy; also gained 0.0017 MSE value, 0.0422 RMSE value for wind power forecasting and 0.0003 MSE, 0.0174 RMSE for wind speed forecasting. [ABSTRACT FROM AUTHOR]

This study presents an improved data-centric approach to short-term water demand forecasting using univariate time series from water reservoir levels. The dataset comprises water level recordings from 21 reservoirs in Eastern Thessaloniki collected over 15 months via a SCADA system provided by the water company EYATH S.A. The methodology involves data preprocessing, anomaly detection, data imputation, and the application of predictive models. Techniques such as the Interquartile Range method and moving standard deviation are employed to identify and handle anomalies. Missing values are imputed using LSTM networks optimized through the Optuna framework. This study emphasizes a data-centric approach in deep learning, focusing on improving data quality before model application, which has proven to enhance prediction accuracy. This strategy is crucial, especially in regions where reservoirs are the primary water source, and demand distribution cannot be solely determined by flow meter readings. LSTM, Random Forest Regressor, ARIMA, and SARIMA models are utilized to extract and analyze water level trends, enabling more accurate future water demand predictions. Results indicate that combining deep learning techniques with traditional statistical models significantly improves the accuracy and reliability of water demand predictions, providing a robust framework for optimizing water resource management. [ABSTRACT FROM AUTHOR]

Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the Walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey's energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO2), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements. [ABSTRACT FROM AUTHOR]

Buildings are complex assets, characterized by environments and uses that change over time, variable occupancies, and long life cycles. They have high operational costs, mostly due to their energy requirements, and account for 30% to 40% of global greenhouse gas emissions. Consequently, substantial effort has been made to forecast their energy needs, with the scope of optimizing their economic and environmental impact. In this regard, the available literature focuses mainly on short-term modeling through the implementation of sets of physics-based equations (i.e., white-box), functional relationships between input and output variables (i.e., black-box), or a combination of both (i.e., grey-box). On the other hand, more research is required on long-term forecast models with the aim of reducing the energy needs. Within this context, this article presents an original automatic procedure for forecasting the energy needs of buildings in short- and long-term time horizons. This is accomplished by scaling an unknown facility from a similar facility that is already known and by executing a black-box approach based on machine learning algorithms. The proposed method is implemented in real case studies in Italy, predicting the energy needs (i.e., heating, cooling, and electricity) of Sant'Anna Hospital in Ferrara using the historical data of Ca' Foncello Hospital in Treviso. The results show an adjusted coefficient of determination above 0.7 and an average error below 10% for all the energy vectors, demonstrating a feasible forecast performance with a low training set-to-test set ratio. [ABSTRACT FROM AUTHOR]

Along with the growing renewable energy sources sector, energy storage will be necessary to stabilize the operation of weather-dependent sources and form the basis of a modern energy system. This article presents the possibilities of using energy storage in the energy market (day-ahead market and balancing market) in the current market conditions in Poland after reforming the balancing market in June 2024. The current state of the markets is characterized by high price volatility, which can ensure the high profitability of storage operations. However, very flexible and self-adaptive algorithms for charging and discharging are required, taking advantage of market price spreads. This study aimed to see if, through a solution based on ChatGPT 4o, energy storage operations can be planned by taking maximum advantage of the existing price spreads in the market. Previous analyses in this area have focused on complex models that predicted prices in the markets and planned the plant's operation on this basis. In this case, the simple model used (charging and discharging based on historical prices) resulted in profits of EUR 90/MWh, while in the second case, when holidays, weather, and demand forecasts were taken into account, the profit was EUR 150–180/MWh, which exceeds the current Levelized Cost of Electricity of storage estimated at around EUR 100/MWh. These analyses indicated that modern genAI tools are appropriate for further study, especially as the technology dramatically increases its capabilities. [ABSTRACT FROM AUTHOR]

According to most energy demand forecasts, woody biomass has the potential to become an important source of renewable energy, especially during the transitional period of energy transition. The aim of this article was to estimate the energy potential of the biomass from the forest and the biomass generated by the mechanical processing of wood raw material and also to show the spectrum of possibilities for the potential use of the biomass for energy production in Poland. This research used available statistical and literature data on the species structure of harvested wood and the qualitative and assortment structures of woody biomass. The basic parameters of the raw material were evaluated in accordance with the EU classification of energy wood. This study confirmed the relationship between the energy potential of woody biomass and energy demand in Poland. The correlation coefficient for these variables was r = 0.984. This correlation was reflected in the significant shares of biomass in the production of electricity (more than 9%) or heat (almost 14%). Energy wood resources in Poland are smaller than in other European Union countries, which affects the scale of the potential use of woody biomass for energy purposes. Nevertheless, the use of such a biomass is fully justified from the point of view of possible development. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic affected the lives of all humans in their daily routines, and the transportation sector became one of the most affected services worldwide. Different instruments are used to enable the effect of the pandemic on transportation to be measured. In this study, trip generation modelling is performed first to understand what factors trigger trip making and how they affect rail passenger flow (RPF) in general; the aim is to measure the loss in RPF due to the Covid-19 pandemic using these trip generation models. Daily RPF data from station to station in Türkiye during the years 2011–2019 are used for the trip generation models and 2016 rail passenger survey data are used for understanding the geographical characteristics of the flow. It is seen that instead of one model covering the whole country, it is more suitable to form different models for specific regions. Using region-based trip generation models, the Covid-19 effect on RPF on the mainlines was measured for the year 2020. Modelled values are anticipated numbers for the no-pandemic condition; therefore, by comparing the modelled and observed data, it is possible to understand how Covid-19 affected the traffic. [ABSTRACT FROM AUTHOR]

Rapid advancements in artificial intelligence (AI) are driving transformative changes in many areas, with significant environmental implications. Yet, environmental assessments for specific applications are scarce. This study presents an in‐depth life cycle assessment of "Foodforecast," a machine learning (ML) cloud service designed to reduce food waste in bakeries by optimizing sales forecasting. It covers four impact categories: global warming, abiotic resource depletion, cumulative energy demand, and freshwater eutrophication. The assessment includes both the direct environmental impacts of the ML model and the underlying system hardware, as well as the indirect benefits of avoided bakery returns compared to traditional ordering methods, using real‐world case study data. In 2022, "Foodforecast" led to an average 30% reduction in bakery returns, primarily bread and rolls, according to sales reports. The associated environmental benefits significantly outweighed the system's direct impacts by one to three orders of magnitude across impact categories and return utilization scenarios. The study identifies support activities such as service maintenance during deployment as major direct impact factors, surpassing those from cloud compute for ML operations. Data processing and inference dominate the latter, while the much‐discussed ML training plays a minor role. The environmental consequences of AI are complex and dual sided. This case study demonstrates that AI might provide environmental benefits in certain contexts, yet results are constrained by methodological challenges and data uncertainties. There remains a need for further holistic LCAs across different ML applications to inform decision‐making processes and ultimately guide the responsible use of AI. [ABSTRACT FROM AUTHOR]

Hydrogen will play critical pole in industrial field, heating field and transportation field, which can achieve mutual conversion of different energies. Hydrogen load prediction demand is important for establishing unified energy system, a novel prediction model is established based on particle swarm algorithm (PSA) and grey Ridgelet neural network (GRNN) to improve medium and long term hydrogen load demand prediction accuracy. Firstly hydrogen load demand prediction model in unified energy system is established, which concludes hydrogen load demand prediction models in industrial field, heating field and transportation field, and then total hydrogen demand model is deduced. Secondly, model of GRNN is constructed based on grey system theory and Ridgelet neural network, analysis procedure of GRNN is established. Structure of GRNN is confirmed, and mathematical model is constructed. To enhance prediction effectiveness of GRNN, PSA is used to optimize parameters of GRNN. Finally hydrogen load demand data in a province is selected to carry out prediction simulation, results show that prediction error of proposed PSA-GRNN ranges from 1.88% to 3.02%, which is less than that of other three prediction models, and fit goodness of proposed PSA-GRNN ranges from 0.958 to 0.985, which is also less than that of other three prediction models. Therefore proposed PSA-GRNN has better prediction precision and efficiency, which can obtain better precision effect and applicability. Hydrogen load demand prediction results in heating field based on PSAGRNN are closer to real value than that based on other three prediction models, results show that proposed PSA-GRNN has better prediction accuracy that other three prediction models. [ABSTRACT FROM AUTHOR]

In this study, a hybrid artificial neural network (ANN)-Rao series (Rao_1, Rao_2, and Rao_3) algorithm model was developed to analyze water consumption in Istanbul province, Turkey. A multiple linear regression (MLR) model was developed and an ANN was also trained with back-propagation (BP) artificial bee colony (ABC) algorithms for comparison. Gross domestic product and population data were treated as independent variables. To test the accuracy of the presently developed hybrid model, its outputs were compared with those of ANN-BP, ANN-ABC, and MLR models. Error values calculated for the test set indicated that the ANN-Rao_3 algorithm outperformed the MLR, ANN-BP, and ANN-ABC reference models as well as ANN-Rao_1 and ANN-Rao_2 algorithms. Therefore, using the ANN-Rao_3 model, water consumption forecasts for Istanbul province were generated out to 2035 for low-, expected-, and high-water demand conditions. The model-generated forecasts indicate that the water requirements of Istanbul in 2035 will be between 1182.95 and 1399.54 million m3, with the upper-range estimates outpacing supplies. According to low and expected scenarios, there will be no problem in providing the water needs of Istanbul until 2035. However, according to high scenario, water needs of Istanbul will not be provided as of 2033.Therefore, water conservation policies should be enacted to ensure provision of the water needs of Istanbul province from 2033 onward. [ABSTRACT FROM AUTHOR]

Increasingly, the manufacturers and third-party sellers sell their products online at large e-commerce platforms. A good forecast is necessary to avoid resource wastage in the form of over-production or underserved demand. Sensing the demand for a new product on e-commerce platforms in the presence of other competitive products requires techniques beyond traditional forecasting methods. Typical ecommerce platforms, being a two-sided marketplace, need to make a trade-off between showing very relevant and trusted products versus exploring new selections. As customers heavily rely upon organic search functionality for product discovery, the demand forecasting for products depends upon signals from search algorithms. In this paper, we propose an end-to-end Deep Learning (DL) based solution to estimate new product demand. Our design science research (DSR) approach involves the use of search ranking algorithm and its features to simulate the impressions of new products in the presence of existing products and vice versa. This approach helps to estimate demand using simulated search funnel metrics to bake in competitive characteristics of other products. [ABSTRACT FROM AUTHOR]

With emerging learning techniques and large datasets, the advantages of applying deep learning models in the field of tourism demand forecasting have been increasingly recognized. However, the lack of sufficient interpretability has led to questioning the credibility of most existing deep learning models. This study attempts to meet these challenges by proposing an interpretable deep learning framework, which combines the long short-term memory model with Shapley Additive interpretation. Results of two case studies conducted in China confirm that our model can perfectly reconcile interpretability and forecasting accuracy. The study has greatly promoted the development of tourism demand forecasting models and provides important practical implications for improving the ability of management decision making and resource optimization. [ABSTRACT FROM AUTHOR]

Accurate energy consumption forecasting is essential in the decision-making process, and in optimizing energy production and distribution to meet customers' demands, especially given the fluctuating demand. The widespread deployment of smart meters has revolutionized the collection of time-series data on energy consumption, providing detailed insights into usage patterns at a granular level. This paper presents a comprehensive comparison of eight time-series forecasting models: Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA), Decision Trees (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-TermMemory networks (LSTM) to assess the most efficient model on the smart meters' dataset. The models are evaluated using several statistical metrics, and based on the experimental results analysis, the LSTM model provided the best prediction performance with an RMSE of 2.106, a MAPE of 0.19, and a MAE of 1.599. [ABSTRACT FROM AUTHOR]

As the next-generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision-making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, enhancing the accuracy of decision-making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, and outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision-making and economic benefit analysis while offering practical value for real-world applications. • A novel fusion algorithm for accurate load demand prediction in smart grids. • Training were conducted on multiple datasets and validation was performed. • Significant contributions to risk assessment planning in smart grid enterprises. [ABSTRACT FROM AUTHOR]

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). The Seq2Seq model manages uncertainties in renewable energy production and demand forecasts by leveraging historical data and real-time inputs to learn patterns in the temporal evolution of these variables. The attention mechanism embedded in the Seq2Seq architecture focuses on relevant parts of the input sequences, such as past demand spikes or periods of intermittent generation. There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization. [ABSTRACT FROM AUTHOR]