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Electricity is one of the most important needs of human life. In order to provide this need sufficiently, demand for the electricity needs to be predicted in advance. Conducting production oriented studies based on the estimation results is a must. In this study, electricity consumption data of Turkey between the years 1970 and 2014 were collected from Turkish Statistical Institute. Using these data,...
Critical for successful operations of service industries, such as telecoms, utility companies and logistic companies, is the service chain planning process. This involves optimizing resources against expected demand to maximize the utilization and minimize the wastage, which in turn maximizes revenue whilst minimizing the cost. This is increasingly involving the automation of the planning process...
To prevent possible accidents, the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently. A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction. Compared with traditional learning algorithms,...
This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of...
This study sought to investigate the effect of the number of input variables on both the accuracy and the robustness of the artificial neural network (ANN) method for predicting the performance parameters of a solar energy system. Tests were conducted on a solar energy system in Ottawa, Canada during summer under different weather conditions. Three different ANN models, i.e., one each with nine, eight...
In this work a neural network NARX model has been developed in order to predict availability of a heavy duty equipment of an important copper mining site in Chile. Four exogenous inputs have been considered (Number of Detentions, Mean Time to Repair, Mean Time between Failures and Use of Physical Availability) while Availability is the autoregressive variable. A 30 days moving average has been performed...
Accurate machine performance prediction is crucial to an effective maintenance strategy for improved reliability and to reduce total maintenance cost. In this study, a time series neural network based approach is introduced to achieve more accurate and reliable performance prediction of machine using condition monitoring data source. The proposed time series model utilizes the various measured condition...
In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic performance of engineering students. Cumulative Grade Point Average (CGPA) was used to measure the academic...
Diabetes is a common but serious chronic disease. Nearly 8% of Americans who are aged 65 and older (about 10.9 million) suffer from this deadly disease. Self-management of this disease is possible, yet the older population lack knowledge, have denial and often lack motivation to do so. Recently we have demonstrated sensor-based network architecture within the home to monitor daily activities and biological...
Prediction of dengue outbreak becomes crucial in Malaysia because this infectious disease remains one of the main health issues in the country. Malaysia has a good surveillance system but there have been insufficient findings on suitable model to predict future outbreaks. While there are previous studies on dengue prediction models in Malaysia, unfortunately some of these models still have constraints...
Traffic jam is a major problem in Bangkok and nearby provinces in Thailand. Currently, there have been several attempts to solve this elevating problem by using GPS together with GPRS technologies in tracking and collecting traffic data from vehicles. In this work, we obtained one-month records of GPS data from 297 volunteered vehicles. Using vehicles' velocity as input, we have developed a travel...
Sunspot area is an important feature to measure the solar activities. Prediction of sunspot area can provide useful information for solar activities and space weather studies etc. In this paper, we propose a smoothed monthly mean sunspot area prediction method using artificial neural network. The prediction model is built by training the area data before the eighteenth solar cycle, and then forecast...
As an unlicensed wireless system, how to discover idle spectrum-bands efficiently and handover to minimize interferences to primary (licensed) users is the main focus for Cognitive Radio (CR). Therefore, the prerequisite for being “cognitive” lies in a deeper understanding of the characteristics of current spectrum behavior, such as a better model for spectrum behavior prediction, so as to design...
The features of a short-term prediction of a stock price using a multi-layer perceptron in a moving simulation application mode are considered in this paper. The input data for the short-term prediction mode are analyzed. The architecture of the predicting model is developed. The simulation modeling results show a high accuracy of the prediction on the historical stock prices of Fiat company.
Wind power's volatility and intermittence have a profound impact on power system's security and economic operation. However, high-precision power prediction is the important prerequisite to reduce the influence of wind power on the power system. This paper illustrates a wind power prediction model based on time-series and back propagation artificial neural network (BP-ANN), considering wind speed,...
In this investigation we applied the Multi Layer Perceptron (MLP) neural networks for modeling and predicting a real non Gaussian process. The obtained results show that an agreement between predicted and measured values. The statistical error analysis used to evaluate the performance of the correlations, between measured and predicted values provides satisfactory results. The developed model is tested...
Artificial Neural networks ANNs are dynamic systems which have the ability not only to capture the relationship between input and output parameters of complex systems but also highly effective when there is no any mathematical formula or model for the system. Therefore, they are very potential and appropriate for design of systems whose functions cannot be expressed explicitly in the form of mathematical...
Numerous techniques have been suggested for extracting energy from the sea. Tidal current turbines are a convenient method for extracting power from oceanic currents. Tidal turbines share many similarities to wind turbines; however due to the higher density of sea water they can produce 800-900 times more power when compared to an equivalent wind turbine of similar size operating at the same speed...
Soft computing forecasting tools play an important role to forecast many complicated systems. In this paper, an effort has been made to use soft computing approaches to predict Dhaka daily temperatures for the period of 28 February 1945 to 27 August 2006. We have selected the fuzzy neuro model, the neuro genetic algorithm model as soft computing techniques. To compare results, a popular time series...
Predicting revenue from tenants for an enterprise having several malls cannot be easily done using conventional approach, such as spreadsheet or manual calculations. Such an enterprise has abundant data yet inadequate resources to analyze such data. This paper presents the data mining method, namely the Artificial Neural Network (ANN), to predict the revenue based on the previous data. ANN can help...
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