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The power output of solar energy conversion facilities such as photovoltaic systems is highly dependent and proportional to the amount of solar radiation absorbed on the collecting surface. In order to have an efficient design of these systems, it is essential to perform solar resource assessment on the intended location prior to installation. Advancements in computational intelligence led to applications...
This study presents a scalable and robust approach to spatial downscaling in the context of climate downscaling. We explore the ability of four techniques to downscale a climate variable to a given location of interest. As an example, we focus on downscaling daily mean air temperature at twelve stations located across the topographically complex province of British Columbia, Canada. The techniques...
The paper presents some models based on artificial neural networks for particulate matter concentration forecasting. A methodology framework is proposed for selecting the best forecasting model from a set of neural networks models. First, two artificial neural network types (feed forward and radial basis) are analyzed for concentration forecasting of the particulate matter with diameter less than...
In this paper, we present the application of radial basis function (RBF) neural network for aerodynamic parameter estimation. The Two-Stage RBF neural network (NN) architecture is proposed for complete aerodynamic modelling. The RBF NN is trained by hybrid learning using K-means clustering and recursive least squares (RLS) methods. The proposed architecture yields the significant improvements for...
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...
This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle (HFV) models by using neural network (NN) identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbance-observer-based-control (DOBC) algorithm with...
In a building office, an air-conditioning system is one of the systems that contributes most to the electrical energy expense. The ability to predict the short-term electrical energy consumption in an air-conditioning environment can provide valuable information in controlling electrical appliance usages so that the overall energy consumption can be kept at an acceptable level for most of the time...
Industry 4.0 is gaining more attention from the public, and thus the correlation between factories and nearby environmental pollution sources is a subject worth in-depth research. Among environmental issues, Particulate Matter2.5 (PM2.5) has received considerable attention in recent years from academic units and governments, and one of the secondary PM2.5 sources is the complex chemical reaction of...
In this paper using Artificial Neural Network (ANN) are presented forecasting results of PM10 concentrations for the city of Sarajevo. Input data of the proposed model are meteorological variables (wind speed, humidity, temperature and pressure) and pollution variable (PM10 concentration) recorded in the Federal Institute for Hydrometeorology from 2010 to 2013. The proposed model is tested on the...
Air pollution has become health hazard. With the growth of industries, the air quality has now become an issue both for the environment as well to the society over the last few years. Due to the rising degradation of air quality, the need for control has risen. Artificial neural network have been applied to many environmental engineering problems and have demonstrated good degree of success in processing...
To generate reliable forecasts, we need good estimates of both the current system state and the model parameters. Numerical weather prediction (NWP) uses atmospheric general circulation models (AGCMs) to predict weather based on current weather conditions. The process of entering observation data into mathematical model to generate the accurate initial conditions is called data assimilation (DA)....
Application of neural networks for direct prediction of lateral-directional force and moments coefficients from the measured flight data of the research aircraft is proposed in this paper. Proposed model of neural networks appears to be a suitable practical approach to develop relationship between flight variables. This relationship eliminates the need of aerodynamic model as well as thrust model...
This paper introduces artificial neural network (ANN) for long term wind speed prediction. The online available dataset of 26 cities from NASA are used to evaluate the performance of ANN model. Data of 22 cities are used for training the neural network and remaining 4 cities data samples are used for testing purpose. Air temperature, earth temperature, relative humidity, daily solar radiation, elevation,...
One of the purposes of smart grids is the efficient delivery of sustainable, economic and secure electricity supplies. One of the strategies used for this purpose is the control and improvement of overhead lines ampacity. A smart use of the actual ampacity requires the implementation of intelligent control devices. Research on ampacity is aimed not only to calculate it in the real time, but also to...
This paper presents linear system identification results using noisy data from a six-degree-of-freedom aircraft simulation and data obtained from flight test of an Unmanned Aerial System using the recently developed novel Artificial Neural Network System Identification algorithm. The method uses an artificial neural network with a single input layer and single output layer to learn the elements of...
As a natural result of the growing air transportation, the importance of the fligth control systems that simultaneously evaluates the many parameters has increased greatly. This study presents the results of a modeling examination based on the use of Anfis and artificial neural networks for simultaneously determination of speed and fuel parameters of the flight control system. In the study given for...
The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM2.5 hourly measurements. The main statistical parameters...
High accuracy retrieval algorithm is developed for integrated water vapor content (WVC) retrieval from Advanced Microwave Sounding Radiometer 2 (AMSR2) measurements over open ocean areas. The algorithm is based on physical modeling of the brightness temperature (BT) of the upwelling radiation of the atmosphere — ocean system. The brightness temperature inversion is carried out with Neural Networks...
This paper describes an agent based approach for simulating the control of an air pollution crisis. A Gaussian Plum air pollution dispersion model (GPD) is combined with an Artificial Neural Network (ANN) to predict the concentration levels of three different air pollutants. The two models (GPM and ANN) are integrated with a MAS (multi-agent system). The MAS models pollutant sources controllers and...
Measured value of minimum air temperature, maximum air temperature, average air temperature and solar radiation between 1 January 2012 to 31 April 2014 for Hamirpur city in Himachal Pradesh, India are used for prediction of daily global solar radiation (DGSR) with artificial neural network (ANN) technique. The prediction of DGSR are made with three combinations of input variables namely: (i) average...
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