scholarly journals A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 511
Author(s):  
Adeniyi Kehinde Onaolapo ◽  
Rudiren Pillay Carpanen ◽  
David George Dorrell ◽  
Evans Eshiemogie Ojo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Real Life ◽  
Weather Conditions ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Weather Factors ◽  
Weather Events ◽  
Artificial Neural

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

scholarly journals Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks

2017 ◽  
Vol 42 (4) ◽  
pp. 643-651
Author(s):  
Naveen Garg ◽  
Siddharth Dhruw ◽  
Laghu Gandhi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Technique ◽  
Back Propagation ◽  
National Research Council ◽  
Sound Insulation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Design And Optimization ◽  
Artificial Neural

Abstract The paper presents the application of Artificial Neural Networks (ANN) in predicting sound insulation through multi-layered sandwich gypsum partition panels. The objective of the work is to develop an Artificial Neural Network (ANN) model to estimate the Rw and STC value of sandwich gypsum constructions. The experimental results reported by National Research Council, Canada for Gypsum board walls (Halliwell et al., 1998) were utilized to develop the model. A multilayer feed-forward approach comprising of 13 input parameters was developed for predicting the Rw and STC value of sandwich gypsum constructions. The Levenberg-Marquardt optimization technique has been used to update the weights in back-propagation algorithm. The presented approach could be very useful for design and optimization of acoustic performance of new sandwich partition panels providing higher sound insulation. The developed ANN model shows a prediction error of ±3 dB or points with a confidence level higher than 95%.

Rainfall Prediction by Artificial Neural Networks Trained using Different Climate Variables

2021 ◽  
Author(s):  
Mateus Alexandre da Silva ◽  
Marina Neves Merlo ◽  
Michael Silveira Thebaldi ◽  
Danton Diego Ferreira ◽  
Felipe Schwerz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Back Propagation ◽  
Metropolitan Region ◽  
Climate Variables ◽  
Back Propagation Algorithm ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Belo Horizonte

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

Artificial neural networks back propagation algorithm for cutting force components predictions

2013 ◽  
Vol 14 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Issam Hanafi ◽  
Francisco Mata Cabrera ◽  
Abdellatif Khamlichi ◽  
Ignacio Garrido ◽  
José Tejero Manzanares
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Force ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components

Hybrid Electric Vehicle Emissions Monitoring and Estimation using Artificial Neural Networks: Technical Note

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
K. Sujatha ◽  
V. Karthikeyan ◽  
V. Balaji ◽  
N.P.G. Bhavani ◽  
V. Srividhya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Performance Indicators ◽  
Hybrid Electric Vehicle ◽  
Back Propagation ◽  
Technical Note ◽  
Back Propagation Algorithm ◽  
Highway Traffic ◽  
Artificial Neural ◽  
Optimal Control Action

Power is utilized as the prime fuel for hybrid and module electric vehicles in order to build the productivity of commercial vehicles. This paper forecasts the emission factors utilizing discrete Fourier transform, artificial neural networks and hybridization of back propagation algorithm. The DFT facilitates the extraction of the performance indicators which are otherwise called the features. The coefficients of the power spectrum denote the performance indicators. The ANN learns the pattern for emissions from HEVs using these performance indicators. This ANN based strategy offers an optimal control action to detect and reduce the exhaust gas emissions which are hazardous. These vehicles are provided with automated highway traffic Jam assist. Hence the forecast of these emissions offers increased efficiency of 90% to 100% thereby ensuring optimal operating condition for the hybrid vehicles.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

scholarly journals Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

2019 ◽  
Vol 8 (3) ◽  
pp. 4645-4650
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Bilateral Symmetry ◽  
Automated System ◽  
Back Propagation Algorithm ◽  
Animal Kingdom ◽  
Artificial Neural ◽  
Eukaryotic Organisms

The biological kingdom ‘Animalia’ is composed of multi cellular eukaryotic organisms. Most of the animal species exhibit bilateral symmetry. The hierarchy of biological classification has eight taxonomy ranks. The top position in the hierarchy is occupied by the ‘domain’ and ending with the lowest position occupied by ‘species’. The classification of animal kingdom includes, Porifera, Coelenterata, Platyhelminthes, Aschelminthes, Annelida, Arthropoda, Mollusca, Echinodermata and Chordata. Manual identification of Phylum or class for each and every species, is very tedious, because there exists nearly a millions of species categorized under various classes. Hence an automated system is proposed to be developed using image segmentation and Artificial Neural Networks (ANN) trained with Back Propagation Algorithm (BPA) which is capable of assisting the scientists and researchers for class identification. This system will be useful in Museums and Archeological departments, where a huge variety of species are maintained. The classification efficiency of the proposed system is 89.1%.

scholarly journals Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

2013 ◽  
Vol 14 (1) ◽  
pp. 10-17
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Potable Water ◽  
Back Propagation ◽  
Ann Model ◽  
Data Set ◽  
Test Set ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Comparison Of The Results

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables. Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected. Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

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