دانلود و نمایش مقالات مرتبط با Forecasting::صفحه 7
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نتیجه جستجو - Forecasting

تعداد مقالات یافته شده: 206
ردیف عنوان نوع
61 Optimizing hyperparameters of deep learning in predicting bus passengers based on simulated annealing
بهینه سازی پارامترهای یادگیری عمیق در پیش بینی مسافران اتوبوس مبتنی بر بازپخت شبیه سازی شده-2020
Bus is certainly one of the most widely used public transportation systems in a modern city because it provides an inexpensive solution to public transportation users, such as commuters and tourists. Most people would like to avoid taking a crowded bus on the way. That is why forecasting the number of bus passengers has been a critical problem for years. The proposed method is inspired by the fact that there is no easy way to know the suitable parameters for most of the deep learning methods in solving the optimization problem of forecasting the number of passengers on a bus. To address this issue, the proposed algorithm uses a simulated annealing (SA) to find out a suitable number of neurons for each layer of a fully connected deep neural network (DNN) to enhance the accuracy rate in solving this particular optimization problem. The proposed method is compared with support vector machine, random forest, eXtreme gradient boosting, deep neural network, and deep neural network with dropout for the data provided by the Taichung city smart transportation big data research center, Taiwan (TSTBDRC). Our simulation results indicate that the proposed method outperforms all the other forecasting methods for forecasting the number of bus passengers in terms of the accuracy rate and the prediction time.
Keywords: Bus transportation system | Simulated annealing | Deep learning | Hyperparameter optimization
مقاله انگلیسی
62 A distributed computing framework for wind speed big data forecasting on Apache Spark
چارچوب محاسباتی توزیع شده برای پیش بینی داده های بزرگ سرعت باد در Apache Spark-2020
The randomness of the wind speed leads to the intermittency of wind power, which is a challenge to realize wind power energy as reliable and renewable power. The prediction of wind speed time series can promote the use of wind energy. However, the traditional stand-alone methods are unable to meet the requirements of wind speed big data environments. In the study, a hybrid distributed computing framework on Apache Spark is applied for wind speed big data forecasting. Using the distributed computing strategy, the framework can divide the wind speed big data into RDD groups and operate them in parallel. In the framework, a modified wind speed extreme learning machine predictor is built using the Distributed Computing Process strategy, enhanced by the data decomposition and result reconstruction components on Apache Spark. The experimental results indicate that the proposed distributed computing framework on Spark can forecast wind speed big data in multi-step accurately. Besides, the effectiveness of different components in the framework is verified. It is also proved that the proposed distributed computing framework has a faster computation speed when processing big data, compared to the stand-alone method.
Keywords: Wind speed big data forecasting | Distributed computing | Apache Spark | Multi-step forecasting
مقاله انگلیسی
63 Management Forecast Errors and Corporate Investment Efficiency
خطاهای پیش بینی مدیریت و بهره وری سرمایه گذاری شرکت-2020
This paper examines the relation between management forecast errors and corporate investment efficiency. If managers’ forecasting biases systematically affect both external earnings forecasts and internal investment project payoff forecasts, errors in management earnings forecasts will be linked to corporate investment decisions. Consistent with this conjecture, I find that signed management forecast errors are associated with abnormal investment; specifically, more optimistic forecasts are associated with more over-investment, while more pessimistic forecasts are associated with more under-investment. Furthermore, I find that the link between forecast errors and abnormal investment is stronger when analyst feedback reinforces managers’ forecast errors and when managers display persistent forecast errors. Additional tests that consider managers’ disclosure incentives reveal that the link between forecast errors and abnormal investment is stronger when forecast errors are less likely driven by managers’ intentional distortions.
Keywords: Management earnings forecasts | managerial bias | investment efficiency
مقاله انگلیسی
64 Toward a trans-regional vulnerability assessment for Alps. A methodological approach to land cover changes over alpine landscapes, supporting urban adaptation
به سمت ارزیابی آسیب پذیری فرا منطقه ای برای رشته کوه های آلپ. یک رویکرد روششناختی برای تغییر پوشش زمین نسبت به مناظر کوهستانی ، حمایت از سازگاری شهری-2020
The contribution presents a possible assessment methodology for land cover change over ice and snow, between 1990 and 2018 in the Dolomites and the Alpi Giulie. The methodology aims to build surface atlas to assess the land cover changes. The tool is intended as a support for environmental management, forecasting and, as support for territorial government systems in climate- proof planning processes. In the “business as usual” global warming scenario, ice and snow resources will become one of the most affected subjects by Climate Change, with heavy consequences on ecosystems, urban environments and socioeconomic. Current monitoring and assessment systems are fragmented both by survey methodology and by local distribution. The methodology is developed in using GIS, following remote sensing (RS) processes and spatial analysis tools to manage multispectral satellite images. The process uses spectral signatures from satellite images to identify homogeneous areas in material and morphology. The process takes into account the actual systems of assessment and local socioeconomic exposures. The methodology takes a proactive approach to future hazards and impacts considering their management in alpine habitats to support local administrations. The project develops transboundary assessment techniques and aids the adaptation of planning strategies in the context of Climate Change.
Keywords: Urban planning 1 | Transboundary governance 2 | Remote sensing analysis 3 | Climate change 4 | Adaptation strategies 5 | Alps monitoring 6
مقاله انگلیسی
65 A non-canonical hybrid metaheuristic approach to adaptive data stream classification
یک روش متاوریستی ترکیبی غیر متعارف برای طبقه بندی جریان داده تطبیقی-2020
Data stream classification techniques have been playing an important role in big data analytics recently due to their diverse applications (e.g. fraud and intrusion detection, forecasting and healthcare monitoring systems) and the growing number of real-world data stream generators (e.g. IoT devices and sensors, websites and social network feeds). Streaming data is often prone to evolution over time. In this context, the main challenge for computational models is to adapt to changes, known as concept drifts, using data mining and optimisation techniques. We present a novel ensemble technique called RED-PSO that seamlessly adapts to different concept drifts in non-stationary data stream classification tasks. RED-PSO is based on a three-layer architecture to produce classification types of different size, each created by randomly selecting a certain percentage of features from a pool of features of the target data stream. An evolutionary algorithm, namely, Replicator Dynamics (RD), is used to seamlessly adapt to different concept drifts; it allows good performing types to grow and poor performing ones to shrink in size. In addition, the selected feature combinations in all classification types are optimised using a non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually. PSO allows the types in each layer to go towards local (within the same type) and global (in all types) optimums with a specified velocity. A set of experiments are conducted to compare the performance of the proposed method to state-of-the-art algorithms using real-world and synthetic data streams in immediate and delayed prequential evaluation settings. The results show a favourable performance of our method in different environments.
Keywords: Ensemble learning | Data stream mining | Concept drifts | Bio-inspired algorithms | Non-stationary environments | Particle swarm optimisation | Replicator dynamics
مقاله انگلیسی
66 An integrated predictive energy management for light-duty range-extended plug-in fuel cell electric vehicle
یک مدیریت انرژی پیش بینی یکپارچه برای وسیله نقلیه الکتریکی سلول سوختی با پلاگین با دامنه محدود-2020
A reliable power distribution strategy is of great significance towards the performance enhancement of fuel cell electric vehicles. In this work, a novel model predictive control-based energy management is developed for a fuel cell based light-duty range-extended hybrid electric vehicle. To fulfill the model predictive control framework, a cooperative speed forecasting method based on Markov Chain and fuzzy C-means clustering technique is proposed, which contains multiple predictive sub-models for handling different driving patterns. The final prediction results are obtained by synthesizing the forecasted speed profiles from all sub-models with the quantified fuzzy membership degrees. Besides, an adaptive battery State-of-Charge reference generator is built, which can regulate the SoC depleting rates against various power requirements. Combined with the forecasted speed and SoC reference, the desirable control actions are derived through minimizing the performance index over each finite time horizon. As a result, under the realistic urban-based postal-delivery mission profiles, the proposed strategy can achieve over 3.79% equivalent hydrogen consumption saving and over 40.04% fuel cell power dynamics decrement against benchmark strategy. Moreover, the presented predictive energy management is robust to certain level of trip duration estimation errors, further indicating its suitability for real applications.
Keywords: Energy management strategy | Multi-objective optimization | Velocity forecasting | PHEV | Fuel cell
مقاله انگلیسی
67 Development and application of the Activity-BAsed Traveler Analyzer (ABATA) system
توسعه و کاربرد سیستم تجزیه و تحلیل مسافرتی Activity-BAsed (ABATA)-2020
While advanced technologies and big data are widely used in the transportation study, most transportation plans still rely on some variant of traditional four-step demand forecasting models. The most significant limitations of the four-step model are spatiotemporal aggregation of data and difficulty of considering individual travel behaviors. To address these drawbacks, activity-based modeling systems have increasingly been developed. In this paper, we present a new activity-based analytical system, called Activity-BAsed Traveler Analyzer (ABATA). The distinguishing feature of ABATA is the simulation of the present hourly service population that is determined from mobile phone data instead of a synthetic population. ABATA comprises multiple components, including an hourly total population estimator, activity profile constructor, hourly activity population estimator, spatial activity population estimator, and origin–destination estimator. To demonstrate the proposed method, a future aged society in Gangnam, Korea is evaluated as a case study. The results indicate that the hourly activity populations engaged in work, school, and private education decreased, while those engaged in home, shopping, recreation and other activities increased with the aging of the population. The associated changes in mobility were found to be rational and reasonable: older people tend to have a more flexible working time, make shorter-distance trips, undertake more trips for shopping, recreation, home, and other activities, and finish their trips earlier, before evening. The proposed ABATA system is expected to provide a valuable tool for simulating the impacts of future changes in population, activity schedules, and land use on activity populations and travel demands.
Keywords: Activity-based model | Mobile phone data | Hourly service population | Mobility | Aged society
مقاله انگلیسی
68 Remote Human-to-Machine Distance Emulation through AI-Enhanced Servers for Tactile Internet Applications
شبیه سازی فاصله انسان از ماشین از راه دور از طریق سرورهای تقویت شده هوش مصنوعی برای برنامه های لمسی اینترنت-2020
We alleviate the master-slave distance limitation of human-to-machine applications by forecasting and pre-empting haptic feedback transmission. Results show 99% accuracy in detecting touch events and 96% accuracy in forecasting feedback from different slave materials.
مقاله انگلیسی
69 Towards efficient energy management in smart grids considering microgrids with day-ahead energy forecasting
مدیریت انرژی کارآمد در شبکه های هوشمند با توجه به ریز شبکه با پیش بینی انرژی روزانه-2020
This study proposes an efficient energy management method to systematically manage the energy consumption in the residential area to alleviate the peak to average ratio and mitigate electricity cost along with user comfort maximization. We developed an efficient energy management scheme using mixed integer linear programming (MILP), which schedules smart appliances and charging/discharging of electric vehicles (EVs) optimally in order to mitigate energy costs. In the proposed model, consumer is able to generate its own energy from microgrid consisting of solar panels and wind turbines. We also consider an energy storage system (ESS) for efficient energy utilization. This work also performs energy forecasting using wind speed and solar radiation prediction for efficient energy management. Moreover, we perform extensive simulations to validate our developed MILP based scheme and results affirm the effectiveness and productiveness of our proposed energy efficient technique.
Keywords: Artificial neural network | Efficient energy utilization | Energy forecasting | Home energy management | Mixed integer linear programming | Renewable energy generation
مقاله انگلیسی
70 Hydrological modelling of karst catchment using lumped conceptual and data mining models
مدل سازی هیدرولوژیکی حوضه کارست با استفاده از مدلهای مفهومی و داده کاوی بهم چسبیده-2019
Hydrological modelling is a challenging and significant issue, especially in nonhomogeneous catchments in terms of geology, and it is an essential part of water resources management. In this study, daily rainfall-runoff modelling was carried out using the lumped conceptual model, the artificial neural network (ANN), the deepneural network (DNN), and regression tree (RT) data mining models for the nonhomogeneous karst Ljubljanica catchment and four of its sub-catchments in Slovenia with different geological characteristics. Model performance was evaluated using several performance criteria and additional investigation of low and high flows was carried out. The results of the study indicate that the Génie Rural à 4 paramètres Journalier (GR4J) lumped conceptual model yielded better modelling performance compared to the data-driven models, namely ANN, DNN and RT models. Moreover, the enhanced version of the GR4J model (i.e. GR6J) also yielded good performance in terms of the recession part. The RT model yielded the worst performance regarding runoff forecasting among the examined models in the case of all five investigated catchments. However, ANN and DNN data-driven models were slightly more successful in modelling the hydrograph recession in the case of karst sub-catchments compared to the GR4J lumped conceptual model structure. Inclusion of additional meteorological variables to ANN and DNN does not significantly improve modelling results.
Keywords: Hydrological model | Lumped conceptual model | Data mining | Karst | Nonhomogeneous catchment | Ljubljanica River
مقاله انگلیسی
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