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Artykuły w czasopismach na temat „Demand prediction”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Zeng, Lingchao, Cheng Zhang, Pengfei Qin, Yejun Zhou, and Yaxing Cai. "One Method for Predicting Satellite Communication Terminal Service Demands Based on Artificial Intelligence Algorithms." Applied Sciences 14, no. 14 (2024): 6019. http://dx.doi.org/10.3390/app14146019.

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This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2Seq and LSTM networks, the method improves prediction accuracy and applicability, especially for mobile terminals such as aviation and maritime ones. Unlike traditional approaches, it does not require extensive statistical data and can be generalized to real-world systems, providing stable long-term traffic demand predictions. This study utilizes real-world flight data mappe

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Kachalla, Ibrahim Ali, and Christian Ghiaus. "Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions." Energies 17, no. 2 (2024): 443. http://dx.doi.org/10.3390/en17020443.

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Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of

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3

Thiagarajan, Rajesh, Mustafizur Rahman, Don Gossink, and Greg Calbert. "A Data Mining Approach To Improve Military Demand Forecasting." Journal of Artificial Intelligence and Soft Computing Research 4, no. 3 (2014): 205–14. http://dx.doi.org/10.1515/jaiscr-2015-0009.

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Abstract Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this,

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4

Chen, Rongbo, Xiaoming Zhong, and Xinyuan Xu. "Bayesian Neural Network-Based Demand Forecasting for Express Transportation." Highlights in Science, Engineering and Technology 68 (October 9, 2023): 259–65. http://dx.doi.org/10.54097/hset.v68i.12078.

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The rapid development of e-commerce in recent years has driven the growth of the logistics service industry, which in turn has led to a significant increase in express delivery volume. Predicting express delivery volume accurately and in advance can help companies allocate various resources reasonably and provide the basis for predicting express delivery demand. To predict the specific transport volume of XX Express Company's logistics routes on April 28th and 29th, 2023, this article builds two Bayesian prediction models based on the company's historical transportation data as the training se

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Chen, Zhiju, Kai Liu, and Tao Feng. "Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects." Journal of Advanced Transportation 2022 (April 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/7690309.

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The accurate short-term travel demand predictions of ride-hailing orders can promote the optimal dispatching of vehicles in space and time, which is the crucial issue to achieve sustainable development of such dynamic demand-responsive service. The sparse demands are always ignored in the previous models, and the uncertainties in the spatiotemporal distribution of the predictions induced by setting subjective thresholds are rarely explored. This paper attempts to fill this gap and examine the spatiotemporal sparsity effect on ride-hailing travel demand prediction by using Didi Chuxing order da

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Kim, Sujae, Sangho Choo, Gyeongjae Lee, and Sanghun Kim. "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method." Sustainability 14, no. 5 (2022): 2564. http://dx.doi.org/10.3390/su14052564.

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The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters

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7

Tian, Wen, Ying Zhang, Yinfeng Li, and Huili Zhang. "Probabilistic Demand Prediction Model for En-Route Sector." International Journal of Computer Theory and Engineering 8, no. 6 (2016): 495–99. http://dx.doi.org/10.7763/ijcte.2016.v8.1095.

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Lee, Eunkyeong, Hosik Choi, and Do-Gyeong Kim. "PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport." Journal of Advanced Transportation 2023 (January 5, 2023): 1–13. http://dx.doi.org/10.1155/2023/7152010.

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To provide an efficient demand-responsive transport (DRT) service, we established a model for predicting regional movement demand that reflects spatiotemporal characteristics. DRT facilitates the movement of restricted passengers. However, passengers with restrictions are highly dependent on transportation services, and there are large fluctuations in travel demand based on the region, time, and intermittent demand constraints. Without regional demand predictions, the gaps between the desired boarding times of passengers and the actual boarding times are significantly increased, resulting in i

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9

RUPADEVI, RUPADEVI. "Electric Vehicle Energy Demand Prediction: A Critical and Systematic Overview." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03035.

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Abstract: Accurately predicting energy demand is crucial for managing charging infrastructure, maximising vehicle performance, and guaranteeing effective energy distribution as EV adoption picks up speed. This study offers a thorough and organised analysis of EV energy demand prediction methods, covering deep learning frameworks, machine learning models, and conventional statistical methods. It also presents a useful implementation using a web application built with Flask that forecasts EV energy use depending on variables like speed, temperature, battery capacity, and distance travelled. In o

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10

Ma, Yuanzheng, Bing Lv, Yuanfa Wang, and Changyu Shi. "Crop Water Requirement Prediction Method Based on EEMD-Attention-LSTM Model." Journal of Physics: Conference Series 2637, no. 1 (2023): 012028. http://dx.doi.org/10.1088/1742-6596/2637/1/012028.

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Abstract Crop water demand prediction is an important part of Precision agriculture. Due to the nonlinear relationship between input variables (weather data, soil moisture, and crop type) and output variables (crop water demand), it is difficult to accurately predict crop water demand. This article proposes a method for predicting crop water demand based on the EEMD Attention LSTM model. The model combines the ensemble empirical mode decomposition (EEMD), attention mechanism (Attention), and Long short-term memory (LSTM) neural networks to capture the changes in different scales of input varia

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11

Mi, Chunlei, Shifen Cheng, and Feng Lu. "Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks." ISPRS International Journal of Geo-Information 11, no. 3 (2022): 185. http://dx.doi.org/10.3390/ijgi11030185.

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Predicting taxi-calling demands at the urban area level is vital to coordinate the supply–demand balance of the urban taxi system. Differing travel patterns, the impact of external data, and the expression of dynamic spatiotemporal demand dependence pose challenges to predicting demand. Here, a framework using residual attention graph convolutional long short-term memory networks (RAGCN-LSTMs) is proposed to predict taxi-calling demands. It consists of a spatial dependence (SD) extractor, which extracts SD features; an external dependence extractor, which extracts traffic environment-related f

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12

Irfan, Muhammad, Ahmad Shaf, Tariq Ali, et al. "Multi-region electricity demand prediction with ensemble deep neural networks." PLOS ONE 18, no. 5 (2023): e0285456. http://dx.doi.org/10.1371/journal.pone.0285456.

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Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and

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13

Brahimi, Nihad, Huaping Zhang, and Zahid Razzaq. "Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction." ISPRS International Journal of Geo-Information 14, no. 4 (2025): 163. https://doi.org/10.3390/ijgi14040163.

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Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. It offers a comprehensive framework for the integration of various data. The eX-STIN model enhances the previous one by utilizing Ensemble Empirical Mode Decomposition (EEMD), which results in

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14

Li, Jiale, Li Fan, Xuran Wang, Tiejiang Sun, and Mengjie Zhou. "Product Demand Prediction with Spatial Graph Neural Networks." Applied Sciences 14, no. 16 (2024): 6989. http://dx.doi.org/10.3390/app14166989.

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In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveragin

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15

Tang, Li Fang. "CPSO-SVM Based Petroleum Demand Prediction." Applied Mechanics and Materials 273 (January 2013): 91–96. http://dx.doi.org/10.4028/www.scientific.net/amm.273.91.

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Study of oil demand, oil demand uncertainty, leading to its strong non-linear, sudden change characteristic, causes the linear modeling of traditional method and neural network prediction precision is low. In order to accurately forecast demand, presents a chaos particle swarm optimization of support vector machine oil demand forecasting method (CPSO-SVM). The CPSO SVM parameter optimization, and then using SVM to petroleum demand nonlinear variation modeling, finally to 1989~ 2007 oil demand data for simulation, the results show that, compared with other oil demand forecast algorithm, CPSO-SV

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16

Lekidis, Alexios, and Elpiniki I. Papageorgiou. "Edge-Based Short-Term Energy Demand Prediction." Energies 16, no. 14 (2023): 5435. http://dx.doi.org/10.3390/en16145435.

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The electrical grid is gradually transitioning towards being an interconnected area of the smart grid, where embedded devices operate in an autonomous manner without any human intervention. An important element for this transition is the energy demand prediction, since the needs for energy have substantially increased due to the introduction of new and heavy consumption sources, such as electric vehicles. Accurate energy demand prediction, especially for short-term durations (i.e., minutes to hours), allows grid operators to produce the substantial amount needed to satisfy the demand–response

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Jiang, Aiping, Junjun Gao, Ying Wan, Xinyi Zhao, and Siqi Shan. "Intermittent Prediction Method Based On Marcov Method And Grey Prediction Method." European Scientific Journal, ESJ 12, no. 15 (2016): 81. http://dx.doi.org/10.19044/esj.2016.v12n15p81.

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This paper concentrates on the intermittent demand for electric power supply and studies the method of demand prediction. This chapter first divides the demand for electric power supply into two statistical sequences: (1) sequence of demand occurrence, among which “1”stands for the occurrence of demand,“0”means that the demand fails to occur; (2) sequence of demand quantity. Next the author predicts the moment of time and the number of times n that demand occurs within a specific time interval in the future based on 0-1 sequence using Markov arrival process (MAP). Then the paper forecasts the

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18

Li, Jun, Yijun Dong, Qiuxuan Wang, and Chunlu Liu. "Proactive pricing strategies for on-street parking management with physics-informed neural networks." International Journal of Strategic Property Management 28, no. 5 (2024): 320–33. http://dx.doi.org/10.3846/ijspm.2024.22233.

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Effective pricing is important for on-street parking management and proactive parking pricing is an innovative strategy to achieve optimal parking utilization. For proactive parking pricing, accurately predicting parking occupancy and deriving the price elasticity of parking demand are necessary. In recent years, there have been an increasing number of studies applying big data technology for parking-occupancy prediction. However, existing research has not incorporated economic knowledge into modeling, thus preventing application of the price elasticity of parking demand. In this study, proact

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19

Juveria, Khan, Rao Jyoti, and Patil Pramod. "Prediction of Future Electric Energy Consumption using Machine Learning Framework." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3347–50. https://doi.org/10.35940/ijeat.C5829.029320.

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In the last few years, the expanding energy utilization has imposed the formation of solutions for saving electricity. Of many solutions, one is generating a power saving policies which is defined as prediction of energy in smart environments. This model is built, based on the idea that the building residences are provided with smart meters to monitor energy consumption and can be managed accordingly. Recent prediction models focuses on performance of the prediction, but for developing a reliable energy system, it is required to predict the demand taking into account different scenarios. In th

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Ramana, Dr A. Venkata. "Taxi Demand Prediction using ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3811–15. http://dx.doi.org/10.22214/ijraset.2022.43912.

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Abstract: Taxi plays a crucial role in transportation especially in urban areas.Predicting the future demand for taxis in particular geographical location will greatly help internet based transportation companies like Ola, Uber etc. So that we can drastically decrease the waiting time of customers/passengers and also it helps taxi drivers to move to particular location where demand is high eventually making passengers,drivers and companies happy. In this Project we like to predict the demand for taxi in particular location for next 10 min using previous time series data .we want to perform thi

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21

Sachdeva, Purnima, and K. N. Sarvanan. "Prediction of Bike Sharing Demand." Oriental journal of computer science and technology 10, no. 1 (2017): 219–26. http://dx.doi.org/10.13005/ojcst/10.01.30.

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Bike sharing systems have been gaining prominence all over the world with more than 500 successful systems being deployed in major cities like New York, Washington, London. With an increasing awareness of the harms of fossil based mean of transportation, problems of traffic congestion in cities and increasing health consciousness in urban areas, citizens are adopting bike sharing systems with zest. Even developing countries like India are adopting the trend with a bike sharing system in the pipeline for Karnataka. This paper tackles the problem of predicting the number of bikes which will be r

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Mohammadi, Milad, Song Han, Tor M. Aamodt, and William J. Dally. "On-Demand Dynamic Branch Prediction." IEEE Computer Architecture Letters 14, no. 1 (2015): 50–53. http://dx.doi.org/10.1109/lca.2014.2330820.

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B. Rupadevi and Sambaiahpalem Adikesavulu. "Electric Vehicle Energy Demand Prediction Techniques: A Critical and Systematic Review." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 98–101. https://doi.org/10.47001/irjiet/2025.iccis-202515.

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Abstract - Accurately predicting energy demand is crucial for managing charging infrastructure, maximising vehicle performance, and guaranteeing effective energy distribution as EV adoption picks up speed. This study offers a thorough and organised analysis of EV energy demand prediction methods, covering deep learning frameworks, machine learning models, and conventional statistical methods. It also presents a useful implementation using a web application built with Flask that forecasts EV energy use depending on variables like speed, temperature, battery capacity, and distance travelled. In

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Wan, Kun Yang. "Research on Urban Water Demand Prediction." Advanced Materials Research 594-597 (November 2012): 2037–40. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.2037.

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Water demand prediction adopts combined prediction method based on BP neural network prediction model, grey G (1,1) prediction model, time sequence prediction model (second multinomial exponential smoothing model) and single linear regression model (Cubics Ratio model). Empirical results show that combined prediction method makes comprehensive use of information of every separate prediction model, and thus enhances prediction accuracy.

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Xue, Xiang Hong, Xiao Feng Xue, and Lei Xu. "Study on Improved PCA-SVM Model for Water Demand Prediction." Advanced Materials Research 591-593 (November 2012): 1320–24. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1320.

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construct an improved water demand prediction model for support vector machine (SVM) on the basis of principle components analysis (PCA) in order to improve the accuracy of water demand prediction and prediction efficiency. Analyze the principal components of all the index factors which affect water demand; eliminate redundant information between the indices, thus to reduce SVM input dimensions; besides, it also introduces genetic algorithm, solved the problem that the traditional SUV parameters cannot optimized dynamically. A simulated experiment proves that the predication accuracy of this m

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Liu, Hao, Qiyu Wu, Fuzhen Zhuang, Xinjiang Lu, Dejing Dou, and Hui Xiong. "Community-Aware Multi-Task Transportation Demand Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 320–27. http://dx.doi.org/10.1609/aaai.v35i1.16107.

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Transportation demand prediction is of great importance to urban governance and has become an essential function in many online applications. While many efforts have been made for regional transportation demand prediction, predicting the diversified transportation demand for different communities (e.g., the aged, the juveniles) remains an unexplored problem. However, this task is challenging because of the joint influence of spatio-temporal correlation among regions and implicit correlation among different communities. To this end, in this paper, we propose the Multi-task Spatio-Temporal Netwo

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Lestuzzi, Pierino, and Lorenzo Diana. "Accuracy Assessment of Nonlinear Seismic Displacement Demand Predicted by Simplified Methods for the Plateau Range of Design Response Spectra." Advances in Civil Engineering 2019 (September 19, 2019): 1–16. http://dx.doi.org/10.1155/2019/1396019.

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The nonlinear seismic displacement demand prediction for low-period structures, i.e., with an initial fundamental period situated in the plateau of design response spectra, is studied. In Eurocode 8, the computation of seismic displacement demands is essentially based on a simplified method called the N2 method. Alternative approaches using linear computation with increased damping ratio are common in other parts of the world. The accuracy of three methods for seismic displacement demand prediction is carefully examined for the plateau range of Type-1 soil class response spectra of Eurocode 8.

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Geng, Shaoqing, and Hanping Hou. "Demand Stratification and Prediction of Evacuees after Earthquakes." Sustainability 13, no. 16 (2021): 8837. http://dx.doi.org/10.3390/su13168837.

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In recent years, frequent natural disasters have brought huge losses to human lives and property, directly affecting social stability and economic development. Since the driving factor of disaster management operations is speed, it will face severe challenges and tremendous pressure when matching the supply of emergency resources with the demand. However, it is difficult to figure out the demands of the affected area until the initial post-disaster assessment is completed and demand is constantly changing. The focus of this paper is to stratify the evacuation needs and predict the number of ev

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Jiajing, Jiang, Cui Qingan, and Zhu Aoquan. "Research on Gold Demand Prediciton Based on GM-GPR Model." E3S Web of Conferences 253 (2021): 02014. http://dx.doi.org/10.1051/e3sconf/202125302014.

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The prediction system of gold demands in China is faced with issues such as uncertain factors, limited historical data, and nonlinearity. In order to have a more accurate prediction of gold demands, a prediction method based on the integration of grey prediction and Gaussian process regression is proposed. Specifically, equal weights are assigned to each model and a grey prediction is adopted to reflect the uncertain and changing relationship of gold demands, with Gaussian process regression indicating the nonlinear impacts of factors on gold demands. Moreover, modified particle swarm optimiza

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Lin, Adrian Xi, Andrew Fu Wah Ho, Kang Hao Cheong, et al. "Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction." International Journal of Environmental Research and Public Health 17, no. 11 (2020): 4179. http://dx.doi.org/10.3390/ijerph17114179.

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The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning t

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Duan, Ganglong, and Jiayi Dong. "Construction of Ensemble Learning Model for Home Appliance Demand Forecasting." Applied Sciences 14, no. 17 (2024): 7658. http://dx.doi.org/10.3390/app14177658.

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Given the increasing competition among household appliance enterprises, accurately predicting household appliance demand is crucial for enterprise supply chain management and marketing. This paper proposes a combined model integrating deep learning and ensemble learning—LSTM-RF-XGBoost—to assist enterprises in identifying customer demand, thereby addressing the complexity and uncertainty of the household appliance market demand. In this study, Long Short-Term Memory Network (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models are established separately. Then, the three in

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Chen, Xinran, Meiting Tu, Dominique Gruyer, and Tongtong Shi. "Predicting Ride-Hailing Demand with Consideration of Social Equity: A Case Study of Chengdu." Sustainability 16, no. 22 (2024): 9772. http://dx.doi.org/10.3390/su16229772.

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In the realm of shared autonomous vehicle ride-sharing, precise demand prediction is vital for optimizing resource allocation, improving travel efficiency, and promoting sustainable transport solutions. However, existing studies tend to overlook social attributes and demographic characteristics across various regions, resulting in disparities in prediction fairness between areas with plentiful and limited transportation resources. In order to achieve more accurate and fair prediction, an innovative Social Graph Convolution Long Short-Term Memory framework is proposed, incorporating demographic

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Phithakkitnukooon, Santi, Karn Patanukhom, and Merkebe Getachew Demissie. "Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 773. http://dx.doi.org/10.3390/ijgi10110773.

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Dockless electric scooters (e-scooter) have emerged as a green alternative to automobiles and a solution to the first- and last-mile problems. Demand anticipation, or being able to accurately predict spatiotemporal demand of e-scooter usage, is one supply–demand balancing strategy. In this paper, we present a dockless e-scooter demand prediction model based on a fully convolutional network (FCN) coupled with a masking process and a weighted loss function, namely, masked FCN (or MFCN). The MFCN model handles the sparse e-scooter usage data with its masking process and weighted loss function. Th

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Yu, Xinlian, Ailun Lan, and Haijun Mao. "Short-Term Demand Prediction for On-Demand Food Delivery with Attention-Based Convolutional LSTM." Systems 11, no. 10 (2023): 485. http://dx.doi.org/10.3390/systems11100485.

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Demand prediction for on-demand food delivery (ODFD) is of great importance to the operation and transportation resource utilization of ODFD platforms. This paper addresses short-term ODFD demand prediction using an end-to-end deep learning architecture. The problem is formulated as a spatial–temporal prediction. The proposed model is composed of convolutional long short-term memory (ConvLSTM), and convolutional neural network (CNN) units with encoder–decoder structure. Specifically, long short-term memory (LSTM) networks are a type of recurrent neural network capable of learning order depende

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Liu, Chunjing, Zhen Liu, Jia Yuan, Dong Wang, and Xin Liu. "Urban Water Demand Prediction Based on Attention Mechanism Graph Convolutional Network-Long Short-Term Memory." Water 16, no. 6 (2024): 831. http://dx.doi.org/10.3390/w16060831.

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Predicting short-term urban water demand is essential for water resource management and directly impacts urban water resource planning and supply–demand balance. As numerous factors impact the prediction of short-term urban water demand and present complex nonlinear dynamic characteristics, the current water demand prediction methods mainly focus on the time dimension characteristics of the variables, while ignoring the potential influence of spatial characteristics on the temporal characteristics of the variables. This leads to low prediction accuracy. To address this problem, a short-term ur

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Mauditia, Lyra, Nurfitri Imro'ah, and Wirda Andani. "Prediksi Jumlah Permintaan Darah UTD PMI Kota Pontianak Menggunakan ARIMA-Kalman Filter." Indonesian Journal of Applied Statistics 7, no. 1 (2024): 73. https://doi.org/10.13057/ijas.v7i1.85958.

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<p>Ensuring a sufficient supply of blood is a crucial aspect of providing health services. However, the large demand for blood is sometimes difficult to fulfill for one of the work units in the Indonesian Red Cross (PMI), namely the Blood Transfusion Unit. Therefore, blood demand prediction is needed to assist the blod transfuse unit in preparing sufficient blood stock. This study uses the ARIMA-Kalman Filter model to anticipate the quantity of blood demand for Blood Transfusion Unit PMI. The observations modeled in this study are daily observations of the amount of blood demand with the

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VAN VAERENBERGH, STEVEN, ALBERTO SALCINES MENEZO, and OSCAR COSIDO COBOS. "DEVELOPMENT OF A SHORT-TERM PREDICTION SYSTEM FOR ELECTRICITY DEMAND." DYNA 96, no. 3 (2021): 285–89. http://dx.doi.org/10.6036/9894.

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This article describes the development of a prediction method for the demand for electrical energy of a marketer's customer portfolio. The project is motivated by the economic benefit produced when the entity has accurate estimates of energy demand when buying energy in an electricity auction. The developed system is based on time series analysis and machine learning. As this system was part of a real-world project with data from a real environment, the article focuses on practical aspects of the design and development of system of these characteristics, such as the heterogeneity of data sourc

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Huang, Wei. "The Demand Prediction of Water Capacity for Drinking Water Plant by Artificial Neural Network." International Journal of Oceanography & Aquaculture 8, no. 2 (2024): 1–6. http://dx.doi.org/10.23880/ijoac-16000315.

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With the constant progress of the times and the rapid economic and social development, the demand for water consumption in the Hexi area of Xiangtan City is continuously rising. Ensuring the safety of urban water supply has become a crucial task. In this paper, we explore the effectiveness of the artificial neural network model in predicting water demand, leveraging the operational data from a water plant in Xiangtan. Thirty-three parameters are employed in this study to forecast water capacity. The results of our analysis reveal that the back propagation (BP) neural network model offers a mor

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Gupta, Ashish, and Rishabh Mehrotra. "Joint Attention Neural Model for Demand Prediction in Online Marketplaces." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5170.

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As an increasing number of consumers rely on online marketplaces to purchase goods from, demand prediction becomes an important problem for suppliers to inform their pricing and inventory management decisions. Business volatility and the complexity of factors influence demand, which makes it a harder quantity to predict. In this paper, we consider the case of an online classified marketplace and propose a joint multi-modal neural model for demand prediction. The proposed neural model incorporates a number of factors including product description information (title, description, images), contex

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Monteagudo, Francisco Eneldo López, Jorge de la Torre y. Ramos, Leticia del Carmen Ríos Rodríguez, and Leonel Ruvalcaba Arredondo. "Enhancing Electricity Demand Prediction In Mexico: A Comparative Analysis Of Forecasting Models Using Conformal Prediction." Revista de Gestão Social e Ambiental 18, no. 12 (2024): e010644. https://doi.org/10.24857/rgsa.v18n12-235.

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Objectives: This study aims to identify a reliable prediction model for Mexico's wholesale electricity market to prevent supply-demand imbalances by analyzing three machine learning models with conformal prediction techniques. Theoretical Framework: Electricity demand forecasting traditionally uses SARIMAX models or artificial neural networks (ANN)/deep learning (DL). However, SARIMAX models are sensitive to data perturbations, while ANN/DL models face interpretability and computational challenges. Decision tree-based models (LGBM and CatBoost) have emerged as alternatives, offering potential

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P, Loganathan. "Cloud based Monitoring and Control Automation of Industrial Demand Prediction System." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1808–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202293.

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Takahashi, K., R. Ooka, and S. Ikeda. "Anomaly detection and missing data imputation in building energy data for automated data pre-processing." Journal of Physics: Conference Series 2069, no. 1 (2021): 012144. http://dx.doi.org/10.1088/1742-6596/2069/1/012144.

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Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often cont

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Kim, Jin-Young, and Sung-Bae Cho. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder." Energies 12, no. 4 (2019): 739. http://dx.doi.org/10.3390/en12040739.

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As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a

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Bo, Qiuyu, and Wuqun Cheng. "Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network." Computational Intelligence and Neuroscience 2021 (November 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/7414949.

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In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation are premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions. However, the existing estimation methods are blind, slow, or inaccurate, compared with the index values of the water demand collected in real time from irrigated areas. To solve the problem, this paper innovatively introduces the spatiotemporal features of ecological water demand to the forecast of future water demand by integrating an artificial neural netwo

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Hong, Kairong, Yingying Ren, Fengyuan Li, Wentao Mao, and Xiang Gao. "Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization." Sensors 23, no. 16 (2023): 7182. http://dx.doi.org/10.3390/s23167182.

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Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a crucial role in inventory management and lifecycle quality management for the aftermarket service of large-scale manufacturing enterprises. In real-life applications, however, demand for spare parts occurs randomly and fluctuates greatly, and the demand sequence shows obvious intermittent distribution characteristics. Additionally, due to factors suc

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Faisal, Muhammad, and Ahmad Mutatkin Bakti. "Implementasi Algoritma Monte Carlo Untuk Memprediksi Permintaan Aksesoris Mobil." JURIKOM (Jurnal Riset Komputer) 10, no. 2 (2023): 356. http://dx.doi.org/10.30865/jurikom.v10i2.5907.

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Toko ADS Variasi dan AC Mobil is a shop engaged in the sale of car accessories, repairing and replacing car air conditioning spare parts in Kota Palembang, Toko ADS Variasi dan AC Mobil faces challenges in predicting the demand for the right car accessories to sell so that there is no excess or shortage of stock items. So that a method is needed that can be implemented into the inventory system and sales and purchase records. In this research, the author uses the Monte Carlo Algorithm which will generate predictions of demand for car accessories by considering variations in factors that affect

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Xu, Xiaomei, Zhirui Ye, Jin Li, and Mingtao Xu. "Understanding the Usage Patterns of Bicycle-Sharing Systems to Predict Users’ Demand: A Case Study in Wenzhou, China." Computational Intelligence and Neuroscience 2018 (September 5, 2018): 1–21. http://dx.doi.org/10.1155/2018/9892134.

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Bicycle-sharing systems (BSSs) have become a prominent feature of the transportation network in many cities. Along with the boom of BSSs, cities face the challenge of bicycle unavailability and dock shortages. It is essential to conduct rebalancing operations, the success of which largely depend on users’ demand prediction. The objective of this study is to develop users’ demand prediction models based on the rental data, which will serve rebalancing operations. First, methods to collect and process the relevant data are presented. Bicycle usage patterns are then examined from both trip-based

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Duan, Zihe, Yujia Huo, Jiyuan Jiang, Wei Wang, Xiaocheng Ma, and Jianpei Fu. "Optimization and application of the electricity charge trial calculation technology within the intelligent electricity billing management system." International Journal of Low-Carbon Technologies 19 (2024): 2210–17. http://dx.doi.org/10.1093/ijlct/ctae172.

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Abstract In response to the poor performance of the existing electricity billing management system in optimizing enterprise electricity costs, a method for predicting electricity demand based on the CNN-BiLSTM-Attention model has been proposed. This method has been implemented in the intelligent electricity optimization cloud platform to enhance the prediction accuracy of enterprise power load. The objective of this research is to forecast the continuous variation curve of 24-h demand in a day, in order to calculate the peak demand value and provide users with rational and effective energy eff

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Liu, Dongbo, Jian Lu, and Wanjing Ma. "Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems." Journal of Advanced Transportation 2021 (April 26, 2021): 1–14. http://dx.doi.org/10.1155/2021/6654909.

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One-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal, but using returned vehicles to reduce unnecessary relocation is also one of the important methods. Nowadays, trajectory data and other data are available for real-time prediction for return demand. Based on the return demand prediction, the relocation response can be more reasonable. Thus, the bal

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Duan, Zhimei, Xiaojin Yuan, and Rongfei Zhu. "Energy big data demand prediction model based on fuzzy rough set." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5291–300. http://dx.doi.org/10.3233/jifs-189014.

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Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in t

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