Relevant bibliographies by topics / Demand prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Demand prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Demand prediction"

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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 (July 10, 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 mapped to corresponding satellite beams, allowing the precise prediction of beam-specific traffic demands. The results show that aggregating historical demand data for beams with similar trends achieves an average predictive Mean Squared Error (MSE) of 0.0007 and a maximum MSE fluctuation of 0.014, significantly outperforming predictions based on average values in terms of stability and accuracy. This novel solution for resource management in satellite communication ensures efficient and accurate long-term traffic demand predictions.
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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 (January 16, 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-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.
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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 (July 1, 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, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine cross-correlated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.
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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 set, one for predicting the opening of logistics routes and the other for predicting express delivery quantity. This approach reduces the impact of inaccurate predictions due to logistics routes not being opened properly and improves the accuracy of the prediction model. It provides decision support for optimizing resource layout for express delivery companies.
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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 data recorded in Chengdu, China. To obtain the spatiotemporal characteristics of the travel demand, three hexagon-based deep learning models (H-CNN-LSTM, H-CNN-GRU, and H-ConvLSTM) are compared by setting various threshold values. The results show that the H-ConvLSTM model has better prediction performance than the others due to its ability to simultaneously capture spatiotemporal features, especially in areas with a high proportion of sparse demands. We found that increasing the minimum demand threshold to delete more sparse data improves the overall prediction accuracy to a certain extent, but the spatiotemporal coverage of the data is also significantly reduced. Results of this study could guide traffic operations in providing better travel services for different regions.
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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 (February 23, 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. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.
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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 (December 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 inefficient transportation services with minimal movement and maximum costs. Therefore, it is necessary to establish a regional demand generation prediction model that reflects temporal features for efficient demand response service operations. In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. The proposed model considers a region’s unique characteristics and the influence between regions through spatial information, such as the proximity between regions, convenience of transportation, and functional similarity. In addition, three types of temporal characteristics—adjacent visual characteristics, periodic characteristics, and representative characteristics—were defined to reflect past demand patterns. With the proposed demand forecasting model, measures can be taken, such as having empty vehicles move to areas where demand is expected or encouraging adjustment of the vehicle’s rest time to avoid congestion. Thus, fast and efficient transportation satisfying the movement demand of passengers with restrictions can be achieved, resulting in sustainable transportation.
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RUPADEVI, RUPADEVI. "Electric Vehicle Energy Demand Prediction: A Critical and Systematic Overview." International Scientific Journal of Engineering and Management 04, no. 04 (April 23, 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 order to provide real-time, easily navigable predictions, the system combines a learnt machine learning regressor with a data scaler. The entire pipeline—data preprocessing, model training, application design, and performance evaluation—is described in this paper, providing theoretical understanding and a practical solution for EV energy demand forecasting. Keywords: Electric Vehicle, Energy Demand Prediction, Machine Learning, Deep Learning, Data Preprocessing, Model Evaluation
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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 (November 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 variables. It selectively focuses on the relevant parts of the input sequence when making predictions. The performance of the EEMD attention LSTM model was evaluated on the historical crop water demand data set and compared with other prediction models such as RNN, LSTM, and EMD-LSTM. It shows that the model is superior to other methods in terms of Mean squared error and mean absolute error. When the relationship between input variables and output variables is nonlinear, the EEMD-attention-LSTM model is a powerful tool for crop water demand prediction.
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Dissertations / Theses on the topic "Demand prediction"

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McElroy, Wade Allen. "Demand prediction modeling for utility vegetation management." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117973.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 63-64).<br>This thesis proposes a demand prediction model for utility vegetation management (VM) organizations. The primary uses of the model is to aid in the technology adoption process of Light Detection and Ranging (LiDAR) inspections, and overall system planning efforts. Utility asset management ensures vegetation clearance of electrical overhead powerlines to meet state and federal regulations, all in an effort to create the safest and most reliable electrical system for their customers. To meet compliance, the utility inspects and then prunes and/or removes trees within their entire service area on an annual basis. In recent years LiDAR technology has become more widely implemented in utilities to quickly and accurately inspect their service territory. VM programs encounter the dilemma of wanting to pursue LiDAR as a technology to improve their operations, but find it prudent, especially in the high risk and critical regulatory environment, to test the technology. The biggest problem during, and after, the testing is having a baseline of the expected number of tree units worked each year due to the intrinsic variability of tree growth. As such, double inspection and/or long pilot projects are conducted before there is full adoption of the technology. This thesis will address the prediction of circuit-level tree work forecasting through the development a model using statistical methods. The outcome of this model will be a reduced timeframe for complete adoption of LiDAR technology for utility vegetation programs. Additionally, the modeling effort provides the utility with insight into annual planning improvements. Lastly for later usage, the model will be a baseline for future individual tree growth models that include and leverage LiDAR data to provide a superior level of safety and reliability for utility customers.<br>by Wade Allen McElroy.<br>M.B.A.<br>S.M.
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Zhou, Yang. "Multi-Source Large Scale Bike Demand Prediction." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703413/.

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Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.
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Sun, Rui S. M. Massachusetts Institute of Technology. "Analytics for hotels : demand prediction and decision optimization." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111438.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 69-71).<br>The thesis presents the work with a hotel company, as an example of how machine learning techniques can be applied to improve demand predictions and help a hotel property to make better decisions on its pricing and capacity allocation strategies. To solve the decision optimization problem, we first build a random forest model to predict demand under given prices, and then plug the predictions into a mixed integer program to optimize the prices and capacity allocation decisions. We present in the numerical results that our demand forecast model can provide accurate demand predictions, and with optimized decisions, the hotel is able to obtain a significant increase in revenue compared to its historical policies.<br>by Rui Sun.<br>S.M. in Transportation<br>S.M.
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Svensk, Gustav. "TDNet : A Generative Model for Taxi Demand Prediction." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.

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Supplying the right amount of taxis in the right place at the right time is very important for taxi companies. In this paper, the machine learning model Taxi Demand Net (TDNet) is presented which predicts short-term taxi demand in different zones of a city. It is based on WaveNet which is a causal dilated convolutional neural net for time-series generation. TDNet uses historical demand from the last years and transforms features such as time of day, day of week and day of month into 26-hour taxi demand forecasts for all zones in a city. It has been applied to one city in northern Europe and one in South America. In northern europe, an error of one taxi or less per hour per zone was achieved in 64% of the cases, in South America the number was 40%. In both cities, it beat the SARIMA and stacked ensemble benchmarks. This performance has been achieved by tuning the hyperparameters with a Bayesian optimization algorithm. Additionally, weather and holiday features were added as input features in the northern European city and they did not improve the accuracy of TDNet.
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Lu, Hongwei Marketing Australian School of Business UNSW. "Small area market demand prediction in the automobile industry." Publisher:University of New South Wales. Marketing, 2008. http://handle.unsw.edu.au/1959.4/43027.

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The general aim of this research is to investigate approaches to: •improve small area market demand (i.e. SAMD) prediction accuracy for the purchase of automobiles at the level of each Census Collection District (i.e. CCD); and •enhance understanding of meso-level marketing phenomena (i.e. geographically aggregated phenomena) relating to SAMD. Given the importance of SAMD prediction, and the limitations posed by current methods, four research questions are addressed: •What are the key challenges in meso-level SAMD prediction? •What variables affect SAMD prediction? •What techniques can be used to improve SAMD prediction? •What is the value of integrating these techniques to improve SAMD prediction? To answer these questions, possible solutions from two broad areas are examined: spatial analysis and data mining. The research is divided into two main studies. In the first study, a seven-step modelling process is developed for SAMD prediction. Several sets of models are analysed to examine the modelling techniques’ effectiveness in improving the accuracy of SAMD prediction. The second study involves two cases to: 1) explore the integration of these techniques and their advantages in SAMD prediction; and 2) gain insights into spatial marketing issues. The case study of Peugeot in the Sydney metropolitan area shows that urbanisation and geo-marketing factors can have a more important role in SAMD prediction than socio-demographic factors. Furthermore, results show that modelling spatial effects is the most important aspect of this prediction exercise. The value of the integration of techniques is in compensating for the weaknesses of conventional techniques, and in providing complementary and supplementary information for meso-level marketing analyses. Substantively, significant spatial variation and continuous patterns are found with the influence of key studied variables. The substantive implications of these findings have a bearing on both academic and managerial understanding. Also, the innovative methods (e.g. the SAMD modelling process and the model cube based technique comparison) developed from this research make significant contributions to marketing research methodology.
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Eressa, Muluken Regas. "Probabilistic Models for Demand Supply Prediction in The Eenergy Sector." Electronic Thesis or Diss., Université Gustave Eiffel, 2024. http://www.theses.fr/2024UEFL2005.

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Cette thèse étudie des modèles prédictifs probabilistes basés sur les processus gaussiens et l'apprentissage en profondeur pour la prévision de la demande d'électricité. Étant donné que les processus gaussiens sont des modèles prédictifs basés sur des noyaux, leur performance est limitée par le type, le nombre et la dimension du noyau sélectionné. Pour répondre à ces limitations, premièrement, elle propose une nouvelle technique d'approximation gaussienne qui aborde le goulot d'étranglement computationnel bayésien. Deuxièmement, elle propose un algorithme d'estimation de noyau compositionnel stochastique en utilisant l'approximation gaussienne proposée comme modèle sous-jacent. Troisièmement, elle suit une procédure itérative utilisant la validation croisée pour sélectionner les meilleures combinaisons de noyaux qui expliquent le mieux le modèle de génération des données. De plus, elle tente également de pallier la limitation de l'approche du maximum de vraisemblance, qui est généralement utilisée dans les modèles probabilistes d'apprentissage en profondeur et qui ne garantit pas nécessairement une largeur d'intervalle minimisée et une probabilité de couverture maximisée pour les points prévus. Cette thèse propose un nouvel algorithme d'entraînement pour les réseaux neuronaux. L'algorithme proposé d'estimation des bornes inférieures et supérieures basé sur la distribution englobe la largeur d'intervalle et la probabilité de couverture en tant que mesures de qualité avec des paramètres adaptatifs garantissant les performances nécessaires par rapport à d'autres techniques alternatives. Les approches suggérées renforcent le déploiement des modèles gaussiens et d'apprentissage en profondeur dans le secteur de l'énergie. Le modèle d'estimation des bornes pour un intervalle de prédiction minimisé et une probabilité de couverture maximisée peut aider les fournisseurs d'énergie potentiels à dimensionner les générateurs, ce qui se traduira par un gain de profit marginal. De plus, l'algorithme d'estimation de noyau peut simplifier l'application de l'apprentissage basé sur les noyaux pour ceux qui trouvent la sélection des noyaux vague. Pour les experts, il peut donner un aperçu préliminaire de la structure des noyaux qui pourraient potentiellement s'adapter aux données. La technique d'échantillonnage aléatoire de colonnes pourrait offrir une méthode alternative pour la construction et l'approximation rapide d'un modèle gaussien, scalable à de grandes données. De plus, l'estimation des bornes, en plus de fournir une distribution de prévision aux modèles neuronaux à estimation de point, peut également servir de point de départ pour une formation de modèle probabiliste alternative dans les réseaux neuronaux profonds<br>This thesis investigate probabilistic predictive models based on the Gaussian process and deep learning for electricity demand forecasting. As Gaussian processes are kernel-based predictive models, their performance is constrained by the type, number and dimension of the selected kernel. To address these limitations, first it proposes a new Gaussian approximation technique that address the Bayesian computational bottleneck. Second, it proposes a stochastic compositional kernel estimation algorithm using the proposed Gaussian approximation as the underlying model. Third, it follows an iterative procedure using cross-validation for selecting an optimal combinations of kernels that best explain the data generating model. Furthermore, it also tries to address the limitation of maximum likelihood approach which is usually employed in probabilistic deep learning models and yet fails in guaranteeing a minimized interval width and maximized coverage probability for the forecasted points. This thesis proposes a new training algorithm for neural networks. The proposed distribution based lower upper bound estimation algorithm encompasses interval width and coverage probability as quality metrics with adaptive parameters that guarantee the needed performance compared to other alternative techniques. The suggested approaches enhance the deployment of Gaussian and deep learning models in the energy sector. The bound estimation model for a minimized prediction interval and maximized coverage probability, can help potential energy suppliers in sizing generators which will result in a marginal profit gain. In addition, the kernel estimation algorithm can simplify the application of kernel-based learning to those who find kernel selection vague. To the experienced, it can give a preliminary insight into the structure of the kernels that could potentially fit the data. The randomized column sampling technique could offer an alternative method for a fast Gaussian model building and approximation that is scalable to large data. Furthermore, the bound estimation, in addition to providing a forecast distribution to a point estimate neural models, it can also serve as a good starting point to an alternative probabilistic model training in deep neural nets
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Lönnbark, Carl. "On Risk Prediction." Doctoral thesis, Umeå universitet, Nationalekonomi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-22200.

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This thesis comprises four papers concerning risk prediction. Paper [I] suggests a nonlinear and multivariate time series model framework that enables the study of simultaneity in returns and in volatilities, as well as asymmetric effects arising from shocks. Using daily data 2000-2006 for the Baltic state stock exchanges and that of Moscow we find recursive structures with Riga directly depending in returns on Tallinn and Vilnius, and Tallinn on Vilnius. For volatilities both Riga and Vilnius depend on Tallinn. In addition, we find evidence of asymmetric effects of shocks arising in Moscow and in the Baltic states on both returns and volatilities. Paper [II] argues that the estimation error in Value at Risk predictors gives rise to underestimation of portfolio risk. A simple correction is proposed and in an empirical illustration it is found to be economically relevant. Paper [III] studies some approximation approaches to computing the Value at Risk and the Expected Shortfall for multiple period asset re- turns. Based on the result of a simulation experiment we conclude that among the approaches studied the one based on assuming a skewed t dis- tribution for the multiple period returns and that based on simulations were the best. We also found that the uncertainty due to the estimation error can be quite accurately estimated employing the delta method. In an empirical illustration we computed five day Value at Risk's for the S&amp;P 500 index. The approaches performed about equally well. Paper [IV] argues that the practise used in the valuation of the port- folio is important for the calculation of the Value at Risk. In particular, when liquidating a large portfolio the seller may not face horizontal de- mandcurves. We propose a partially new approach for incorporating this fact in the Value at Risk and in an empirical illustration we compare it to a competing approach. We find substantial differences.
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Wong, Wai Ho. "Predicting Demand in Cloud Computing Environments." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9497.

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Cloud computing is a new computing paradigm that enables elastic on-demand pay-per-use access to shared computational resources. However, there are current limitations on the elasticity of cloud resources specifically deployment delays and mismatches in pricing granularity. Demand prediction - the estimation of future demand - serves to mitigate these limitations of elasticity. The focus of this thesis is the demand predictor that operates within the context of cloud resource management. It fulfils three tasks: 1) learn demand patterns from past demand behaviour; 2) predict upcoming demand from learned patterns; 3) detect changes in demand patterns that require relearning. The cloud context requires that the demand predictor operate without a human expert to provide external knowledge or to screen raw historical signals for noise and anomalies. We propose the method of boosted ensembles as the basis for such a demand predictor, exploiting the method’s ability to self-regulate their learning from a contaminated signal to produce accurate predictors. It is also used to construct intervals for detecting pattern changes. The proposed method is supported with empirical evaluations using real-world and synthetic demand signals. These evaluations show that the boosted ensemble method performs at a comparable level to current state-of-the-art methods.
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Bernhardsson, Viktor, and Rasmus Ringdahl. "Real time highway traffic prediction based on dynamic demand modeling." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112094.

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Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch north of Stockholm. The thesis is focusing on the implementation and evaluation of traffic prediction by running a cell transmission model (CTM) forward in time.This method gives reliable predictions for a prediction horizon of up to 5 minutes. In order to improve the results for traffic predictions, a framework for dynamic inputs of demand and sink capacity has been implemented in the MMS system. The third part of the master thesis presents a model which adjusts the split ratios in a macroscopic traffic model based on driver behavior during congestion.
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Jones, Simon Andrew. "Prediction of demand for emergency care in an acute hospital." Thesis, Kingston University, 2005. http://eprints.kingston.ac.uk/20739/.

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This thesis describes some models that attempt to forecast the number of occupied beds due to emergency admissions each day in an acute general hospital. Hospital bed managers have two conflicting demands: they must not only ensure that at all times they have sufficient empty beds to cope with possible emergency admissions but they must fill as many empty beds as possible with people on the waiting list. This model is important as it could help balance these two conflicting demands. The research is based on data from a district general and a postgraduate teaching hospital in South East London. Several tests indicate that emergency bed occupancy may have a nonlinear underlying data generating process. Therefore, both linear models and nonlinear models have been fitted to the data. At horizons up to 14 days, it was found that there was no statistically significant difference in the errors from the linear and nonlinear models. However at the 35 day forecast horizon the linear model gives the best forecast and tests indicate errors from this model are within 4% of mean occupancy. It is noted that a Markov Switching model gave very good forecasts of up to 4 days into the future. A search of the literature found no previous research that tested emergency bed occupancy for nonlinearities. The thesis ends with a gravity model to predict the change in number of Accident and Emergency (A&E) attendances following the relocation of an A&E Department in South East London.
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Books on the topic "Demand prediction"

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. Demand Prediction in Retail. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1.

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Tomar, Anuradha, Prerna Gaur, and Xiaolong Jin, eds. Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6490-9.

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Tennant, S. T. Short term demand analysis and prediction for control of water supply. Leicester: Leicester Polytechnic, 1987.

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Tennant, Steven Trevor. Short term demand analysis and prediction for control of water supply. Leicester: Leicester Polytechnic, 1987.

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Tennant, S. T. A system description of GIDAP(Graphical Interactive Demand Analysis & Prediction program. Leicester: Leicester Polytechnic, 1986.

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Tennant, S. A system description of GIDAP: (A Graphical Interactive Demand Analysis and Prediction Program). Leicester: Leicester Polytechnic, 1986.

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Coulbeck, B. Development of a demand prediction program for use in optimal control of water supply. Leicester: Leicester Polytechnic, 1985.

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Tennant, S. Test and verification procedures for GIDAP: (A Graphical Interactive Demand Analysis and Prediction Program). Leicester: Leicester Polytechnic, 1986.

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Cronin, David. Patterns in money demand: Indicators and predictions. Dublin: Research and Publications Department, Central Bank of Ireland, 1994.

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de, Jong Gerard, EXPEDITE Consortium, RAND Europe, Rand Corporation, and European Commission. Directorate-General for Energy and Transport., eds. EXPEDITE: EXpert-system based PrEdictions of demand for internal transport in Europe. Santa Monica, CA: RAND, 2003.

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Book chapters on the topic "Demand prediction"

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Common Demand Prediction Methods." In Demand Prediction in Retail, 29–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_3.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Evaluation and Visualization." In Demand Prediction in Retail, 115–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_6.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Clustering Techniques." In Demand Prediction in Retail, 93–114. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_5.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Conclusion and Advanced Topics." In Demand Prediction in Retail, 151–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_8.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Tree-Based Methods." In Demand Prediction in Retail, 69–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_4.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Introduction." In Demand Prediction in Retail, 1–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_1.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Data Pre-Processing and Modeling Factors." In Demand Prediction in Retail, 13–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_2.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "More Advanced Methods." In Demand Prediction in Retail, 129–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_7.

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Rubio-Bellido, Carlos, Alexis Pérez-Fargallo, and Jesús Pulido-Arcas. "Energy Demand Analysis." In Energy Optimization and Prediction in Office Buildings, 31–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90146-6_3.

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Yu, Hang, Zishuo Huang, Yiqun Pan, and Weiding Long. "Energy Demand Analysis and Prediction." In Guidelines for Community Energy Planning, 17–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9600-7_2.

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Conference papers on the topic "Demand prediction"

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Spatha, Myrto M., and Dionisios N. Sotiropoulos. "“Transforming” Electricity Demand Prediction." In 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA), 1–8. IEEE, 2024. https://doi.org/10.1109/iisa62523.2024.10786682.

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Nugroho, Restu, Irene Erlyn Wina Rachmawan, Prananda Kamaluddin Rafif, and Ferrizal. "Integrating Demand Hotspots and Adjusted Spatial Indexing for Urban Taxi Demand Prediction." In 2024 IEEE International Conference on Big Data (BigData), 8786–89. IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825750.

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Kumar, Neeraj, Tanusha Mittal, Hassan M. Al-Jawahry, Saloni Bansal, A. Deepak, N. M. Deepika, and Rohit Kaushik. "Electricity Demand Prediction Through Artificial Intelligence Methods." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), 1–5. IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866136.

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S, Senthil Pandi, Kumar P, Nathaniel Abishek A, and Mohamed Hussain S. "Demand Prediction using AutoML Based Ensemble Algorithm." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE), 520–24. IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10986835.

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Shyamala, Pachila, K. Deepa, and S. V. Tresa Sangeetha. "ML Techniques for Crop Demand Prediction- A Study." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616730.

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Doan, Van Khanh, Dong Nguyen Doan, and Binh Hau Nguyen. "Improving Energy Demand Prediction Use Deep Learning Network." In 2024 9th International Conference on Applying New Technology in Green Buildings (ATiGB), 323–27. IEEE, 2024. http://dx.doi.org/10.1109/atigb63471.2024.10717679.

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Sarhir, Oumaima, Zoubida Benmamoun, and Mouad Ben Mamoun. "Prediction Analysis for Demand Forcasting in Automotive Industry." In 2024 10th International Conference on Optimization and Applications (ICOA), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10754474.

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Zhaxi, Dongzhou. "EMD-SOFTS-based demand prediction for supply chain." In 4th International Conference on Electronic Information Engineering and Data Processing (EIEDP 2025), edited by Azlan Bin Mohd Zain and Lei Chen, 105. SPIE, 2025. https://doi.org/10.1117/12.3067110.

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Weng, Haoyuan. "Demand Prediction Model." In 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/ameii-15.2015.291.

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Dey, Sourav, Arushi Das, Sweta Kumari, and Krishnamoorthy Arasu. "Taxi demand prediction." In ADVANCES IN SUSTAINABLE CONSTRUCTION MATERIALS. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0131324.

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Reports on the topic "Demand prediction"

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Kimboko, Andre. A direct and behavioral travel demand model for prediction of campground use by urban recreationists. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.455.

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Weeks, Melvyn. Machine Learning for Prediction and Causal Inference. Instats Inc., 2022. http://dx.doi.org/10.61700/u0qw7udtxd5iz469.

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This seminar explores machine learning techniques for prediction and causal inference, where a researcher or decision maker needs to make a prediction or understand the impact of an intervention in a heterogenous population. For example, researchers may want to infer the effect of an economic, educational, or public health intervention, or a firm may seek to understand how a change in pricing will impact aggregate demand. In these cases, the interest may be in an average effect, but also how the effect varies over different segments of the population (i.e., heterogeneity in the effect). This seminar will provide you with the tools to undertake such inquiry using machine learning (ML), while ensuring that you understand and can communicate how the methods work for prediction and causal inference. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Weeks, Melvyn. Machine Learning for Prediction and Causal Inference. Instats Inc., 2022. http://dx.doi.org/10.61700/r1qb0f2baf6jj469.

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This seminar explores machine learning techniques for prediction and causal inference, where a researcher or decision maker needs to make a prediction or understand the impact of an intervention in a heterogenous population. For example, researchers may want to infer the effect of an economic, educational, or public health intervention, or a firm may seek to understand how a change in pricing will impact aggregate demand. In these cases, the interest may be in an average effect, but also how the effect varies over different segments of the population (i.e., heterogeneity in the effect). This seminar will provide you with the tools to undertake such inquiry using machine learning (ML), while ensuring that you understand and can communicate how the methods work for prediction and causal inference. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Leis. L51866 Field Studies to Support SCC Life Prediction Model. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 1997. http://dx.doi.org/10.55274/r0010357.

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One objective of this project was to gather and analyze SCC field data on lines being retested for use in assessing the validity of current or future SCC models. The scope of this initial study was limited to colonies of SCC in one valve section of a pipeline that runs from Texas to the northeast of the United States. This valve section had an early history of high pH SCC. The susceptibility since has been controlled through hydrotesting and modifications to the gas compression to meet upstream demand while reducing the discharge temperature. In addition to collecting data to validate models of SCC, data were also developed to evaluate the suitability of a hand-held tool to measure the depth of SCC, because such results can be critical in the use of models in making serviceability and maintenance decisions.
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Sapp, James. Electricity Demand Forecasting in a Changing Regional Context: The Application of the Multiple Perspective Concept to the Prediction Process. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.574.

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Yang, Yu, and Hen-Geul Yeh. Electrical Vehicle Charging Infrastructure Design and Operations. Mineta Transportation Institute, July 2023. http://dx.doi.org/10.31979/mti.2023.2240.

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California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered charging station. The objectives are to increase customers’ satisfaction, reduce the power grid burden, and maximize the profitability of charging stations using state-of-the-art global optimization techniques, machine-learning-based solar power prediction, and model predictive control (MPC). The proposed research has broad societal impacts and significant intellectual merits. First, it meets the demand for green transportation by increasing the number of EV users and reducing the transportation sector’s impacts on climate change. Second, an optimal scheduling tool enables fast charging of EVs and thus improves the mobility of passengers. Third, the designed planning tools enable an optimal design of charging stations equipped with a solar panel and battery energy storage system (BESS) to benefit nationwide transportation system development.
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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&amp;R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&amp;R activities, thus promoting the most cost-effective alternative in LCCA.
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Shapovalov, Yevhenii B., Viktor B. Shapovalov, Fabian Andruszkiewicz, and Nataliia P. Volkova. Analyzing of main trends of STEM education in Ukraine using stemua.science statistics. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3883.

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STEM-education is a modern effective approach that nowadays can be interpreted in very different ways and it even has some modification (STEM/STEAM/STREAM). Anyway, the “New Ukrainian school” concept includes approaches similar to STEM-education. However, there wasn’t analyzed the current state of STEM-education in Ukraine. We propose to analyses it by using SEO analysis of one of the most popular STEM-oriented cloud environment in Ukraine stemua.science. It is proposed to use the cycle for cloud-based educational environments (publishing/SEO analysis/team’s brainstorm/prediction/creation of further plan) to improve their efficiency. It is found, that STEM-based and traditional publications are characterized by similar demand of educational process stakeholders. However, the way how teachers and students found the publication proves that traditional keywords (47.99 %) used significantly more common than STEM keywords (2.67 %). Therefore, it is proved that STEM-methods are less in demand than traditional ones. However, considering the huge positive effect of the STEM method, stemua.science cloud educational environment provides a positive effect on the educational process by including the STEM-aspects during finding traditional approaches of education by stakeholders of the educational process.
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Verma, Monika, Thomas Hertel, and Paul Preckel. Predicting Within Country Household Food Expenditure Variation Using International Cross-Section Estimates. GTAP Working Paper, September 2009. http://dx.doi.org/10.21642/gtap.wp57.

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There is a long and distinguished literature involving demand analysis using international cross-section data. Such models are widely used for predicting national per capita consumption. However, there is nothing in this literature testing the performance of estimated models in predicting demands across the income spectrum within a single country. This paper fills the gap. We estimate an AIDADS model using cross-section international per capita data, and find that it does well in predicting food demand across the income distribution within Bangladesh. This suggests that there may be considerable value in using international cross-section analysis to study poverty and distributional impacts of policies.
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Hunt, Will, and Jacqueline O'Reilly. Rapid Recruitment in Retail: Leveraging AI in the hiring of hourly paid frontline associates during the Covid-19 Pandemic. Digital Futures at Work Research Centre, March 2022. http://dx.doi.org/10.20919/alnb9606.

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Increased demand due to the Coronavirus pandemic created the need for Walmart to onboard tens of thousands of workers in a short period. This acted as a catalyst for Walmart to bring forward existing plans to update the hiring system for store-level hourly paid associates in its US stores. The Rapid Recruitment project sought to make hiring safer, faster, fairer and more effective by removing in-person interviews and leveraging machine learning and predictive analytics. This working paper reports on a case study of the Rapid Recruitment project involving semi-structured qualitative interviews with members of the project team and hiring staff at five US stores. The research finds that while implementation of the changes had been successful and the changes were largely valued by hiring staff, lack of awareness and confidence in some changes threatened to undermine some of the objectives of the changes. Reservations about the pre-employment assessment and the algorithm’s ability to predict quality hires led someusers reviewing more applications than perhaps necessary and potentially undermining prediction of 90-day turnover. Concerns about the ability to assess candidates over the phone meant that some users had reverted to in-person interviews, raising the riskof Covid transmission and potentially undermining the objective of removing the influence of human bias linked to appearance and other factors unrelated to performance. The impact of awareness and confidence in the changes to the hiring system are discussed in relation to the project objectives
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