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Content Delivery Networks (CDNs) are faced with an increasing and time varying demand of video contents. Their ability to promptly react to this demand is a success factor. Caching helps, but the question is: which contents to cache? Considering that the most popular contents should be cached, this paper focuses on how to predict the popularity of video contents. With real traces extracted from YouTube,...
The precise and timely prediction of program popularity is of great value for content providers, advertisers, and broadcast TV operators. This information can be beneficial for operators in TV program purchasing decisions and can help advertisers formulate reasonable advertisement investment plans. Moreover, in terms of technical matters, a precise program popularity prediction method can optimize...
The traditional MAC protocol in ad hoc networks includes carrier sensing to judge the busy/idle state of a channel. However, in airborne tactical networks, the results of carrier sensing are usually inaccurate due to the wide distribution of nodes, large communication distance, and high-dynamic network topology, and the carrier sensing causes a large transmission delay and low channel utilization...
Due to the rapid development of network information, e-commerce has entered the era of big data. From these large data mining the useful information has a high commercial value, especially for short life cycle products, improve it in each stage of life cycle prediction ability, in addition to some of the conventional data mining model depends on the specific data mining platform, and can't realize...
In recent years, many research studies are conducted into the use of smart meters data for developping decision-making tools including both analytical, forecasting and display purposes. Forecasting energy generation or forecasting energy consumption demand are indeed central problems for urban stakeholders (electricity companies and urban planners). These issues are helpful to allow them ensuring...
In the competitive world of modern web applications, performance plays a crucial role. An e-commerce company estimated that every 100ms delay reduces sales by 1 percent, and a popular search engine reported that every 500ms delay in search reduces earnings by 20 percent. The demands from users for these services can vary widely based on factors such as the time-of-day and unexpected events that can...
This work presents a successful application of a new hybrid model in forecasting daily global solar radiation for a site in Spain using time series of solar radiation. The hybrid model incorporates the wavelet transform (WT) and Gaussian process regression (GPR). The WT is used to extract meaningful time-frequency information by decomposing the clearness index time series into a set of well-designed...
In this paper, we propose the concept of the deep prediction model for subcutaneous glucose concentration. The concept is based on several layers of prediction models. One aim of this approach is to eliminate time lag, which is more severe in longer prediction horizons. Thus, the prediction accuracy of the algorithm might be increased, even for longer prediction horizons. The second goal is to create...
A prediction of dengue fever cases by using a predictor of rainfall, the rain days, the house index, and the larva-free number has been done in Jember Regency. The evaluation was done by comparing and calculating the deviation value of the predicted number of cases, as a result of the prediction, to the number of actual cases. This prediction simulation of the number of dengue fever cases is using...
This paper proposes a wind power prediction method based on intrinsic time-scale decomposition (ITD) and least square support vector machine (LS-SVM) to improve the accuracy of wind power forecast. The proposed method employs ITD as a preprocessing method to decompose wind power data into a set of proper rotation components and a monotonous baseline signal. Afterwards, the backward difference of each...
In order to maximize the effectiveness of microgrids sophisticated control strategies are required. In this paper we investigate the performance of a model based power flow optimization controller for a grid connected microgrid consisting of; solar PV, battery storage, thermal generation, and a local electrical load. The controller is based on a mixed integer linear program (MILP) applied in a model...
Modelling patient flow is crucial in understanding resource demand and prioritization. To date, there has been limited work in predicting ward-level discharges. Our study investigates forecasting total next-day discharges from an open ward. In the absence of real-time clinical data, we propose to construct a feature set from patient demographics, ward data and discharge time series to derive a random...
Human activity can serve as an identifier of subject health, behavioral patterns, and personal preferences. With the sudden splurge in mobile and wearable devices, activity data has become more readily available to design useful applications that enhance the users' everyday lives without any obtrusive intervention. This paper focuses on the use of a system identification approach to characterize human...
Investment in financial derivatives becomes popular around the world. In Thailand, the Thailand Futures Exchange (TFEX) which is a place where financial derivatives are traded has also been an attractive recently. One of the common question of investor is what the price of financial instrument will be. There are several models has been used in forecasting option price. The most popular one is the...
This paper proposes a simplified microturbine (MT) model which allows for dynamic heat and power output prediction. Considering the time-scale difference of various dynamic processes occuring within MTs, the electromechanical subsystem is treated as a fast quasi-linear system while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A subspace model identification...
A hybrid model for short-term forecasting of aggregated thermal loads and their load control responses is studied in this paper using field test data. Inputs include temperature measurement and forecast, measured power and control signals. The hybrid model comprises 1) partly physically based forecasting of the responses of the controlled thermal loads and the non-controlled power, and 2) forecasting...
Solar forecasting is a pivotal factor in a viable solar energy deployment to support reliable and cost-effective grid operation and control. This paper proposes a new approach to overcome one of the most significant challenges in solar generation forecasting, i.e., the limited availability of the stationary data sets. This challenge is addressed by converting the non-stationary historical solar irradiance...
Smartphones with various embedded sensors and wirelessly connected external sensors will enable new applications across a wide variety of domains. Continuous or long-term sensing, processing, and communication of sensor data using smartphones will consume a significant amount of energy of the resource-constrained smartphones. Compression techniques, including predictive coding (PC) and compressed...
Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the users, energy wastage, system unreliability and shorter equipment life. Faults need to be early diagnosed to prevent further deterioration of the system behaviour and energy losses. In this paper a model-based approach is used in order to detect important chiller systems faults. First,...
A multiple-model adaptive control algorithm is developed for wind power forecasting based on three forecasting models for very-short term wind power prediction. The performance of the three models and the proposed approach is verified in a wind farm in North China. The case study indicates that the proposed approach can provide reasonable very-short term wind power prediction and outperforms the other...
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