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Modeling the spatial variation of resources is necessary because it gives an estimate of what to expect during their exploration and exploitation. We focus on the spatial modeling of polymetallic nodules found in the deep sea regions of the Clarion-Clipperton zone in the Pacific. The data from this region available in the open domain is sparse, which warrants modeling techniques that can efficiently...
The very short-term bus reactive load forecasting allows the electrical system operator to determine the optimal amount of energy to supply the demand with quality, safety and reliability. With this premise, this paper used a knowledge data discovery approach to handle the forecast, from raw data to results analysis, using the neural network for data mining. Two forecasting models were developed:...
Artificial neural networks (ANN) are among the nonlinear prediction techniques popular in the last two decades. Recent studies show that ANN can be modeled with different training techniques. ANN is usually trained by the backpropagation method (BP). In this study, ANN structures were trained by using artificial bee colony algorithm (ABC) and, weight and bias values were tried to be determined. ABC...
A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Clustering process consist of two steps: finding the global optimum number of clusters...
The present work includes the temporal modeling of the oviposition activity of the Aedes aegypti mosquito, a vector of viral diseases such as Dengue, Chicungunya and Zika, based on time series of data extracted from earth observation satellite images. Unlike previous works, Machine Learning techniques that are capable of capturing nonlinear relationships between variables, such as artificial neural...
The short-term load forecasting study is characterized as an estimative of the consumption pattern ranging from a day to a few months ahead, related to the operation planning, mainly in a Unit Commitment and power supply strategies, for example. Improve load forecasting models, mainly about errors reduction is so important, improving the energy efficiency, reducing energy losses and increasing financial...
In recent years due to increased competition between companies in the services sector, predict churn customer in order to retain customers is so important. The impact of brand loyalty and customer churn in an organization as well as the difficulty of attracting a new customer per lost customer is very painful for organizations. Obtaining a predictive model customer behaviour to plan for and deal with...
Airborne wind turbine technology is rapidly growing in purpose to overcome limitation of wind turbines working at low altitude. The high-altitude wind is strong to efficient power generation. Under varying wind conditions, wind forecasting in real time is necessary to be implemented for flight stabilization and power generation. This study is to investigate three widely-used forecasting models for...
In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In...
Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important...
In recent years, the strong growth in solar power generation industries is requiring an increasing need to predict the profile of solar power production over the day, in order to develop high efficient and optimized stand-alone and grid connected photovoltaic systems. Moreover, the opportunities offered by battery energy storage systems coupled with PV systems, require the load power to be forecasted...
In this study, the estimation performances of Multiple Linear Regression, Random Forest, and Artificial Neural Network are examined comparatively. For comparison of these data mining techniques, the power production data from a Photovoltaic Module was used in the research. In this study, the model was constituted from seven variables. One of the variables is dependent (power) and the others are independent...
In this paper a new artificial neural network (ANN) method is proposed to forecast wind speed forecasting. The use of wind power generation (WPG) is expected to reduce CO2 as the framework of environmental preservation. However, output of WPG is affected by the meteorological conditions significantly. As the first stage of research, this paper focuses on wind speed that affects the output of WPG significantly...
This study aims to present time series-based forecasting for Malaysian crude palm oil prices using neural network algorithms. Daily prices of soy bean oil and currency exchange rates are tested as input features, in addition to crude palm oil prices. Efforts are focused on finding the optimal network structures for the modelling of crude palm oil price forecasting. Neural network structures with an...
With the widespread implementation of Automatic Generation Control (AGC) in regional power grids, large-capacity supercritical and ultra-supercritical (SC/USC) power units are required to participate in peak load regulation frequently and often operate under wide-scope variable load conditions. Since a SC boiler unit is a MIMO strong coupling system with nonlinearity and large time delay characteristics,...
In this study, the wind speed prediction model is created that gives a minimum error for different hidden layer neuron numbers and delay step numbers. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model. The predicted values of wind speed obtained are compared with predicted values of wind speed obtained with filter methods. For different...
Machining parameters influence the energy consumed during machining processes. A reliable prediction model for energy consumption will enable industry to achieving energy saving by optimizing the machining parameters during process planning stage. This paper presents a two-level optimization artificial neural network modelling method to characterizing the relationship between energy consumption and...
Artificial neural network (ANN) has been widely applied in flood forecasting and got good results. However, it can still not go beyond one or two hidden layers for the problematic non-convex optimization. This paper proposes a deep learning approach by integrating stacked autoencoders (SAE) and back propagation neural networks (BPNN) for the prediction of stream flow, which simultaneously takes advantages...
Thailand is the world's largest exporter of cassava. The cassava prices fluctuate because of many factors such as the production cost, economic condition, and price intervention. Therefore, this research aims to propose a forecasting model of cassava price based on the 11-year data (from 2005 to 2015) obtained from the Thai Tapioca Starch Association and Office of Agricultural Economics. Various techniques...
Power output of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical...
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