scholarly journals Different ANN Models for Short Term Electricity Price Forecasting

2019 ◽  
Vol 8 (3) ◽  
pp. 6706-6712
Keyword(s):  
Neural Network ◽  
Market Price ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Price Forecasting ◽  
Market Prices ◽  
Electricity Price Forecasting ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Pool Market

In a deregulated electiricity market, price forecasting is gaining demand with application of Artificial Neural Network (ANN). The paper deals with price forecasting with different ANN models.like Back Propagation Neural Network( BPNN), Radial Bias Function Neural Network (RBFNN) and Genectic Algorithm based Neural Network (GANN). A contextual investigation is made with the downloaded data of the day-ahead pool market prices of the California Pool Market using the above four different ANN models and the results are compared.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

2019 ◽  
Vol 53 (6) ◽  
pp. 27-34
Author(s):  
Tim Chen ◽  
C.Y.J. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Variation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Systems ◽  
Modeling And Forecasting ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

AbstractThe reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.

Offline Signature Recognition using Back Propagation Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 678
Author(s):  
Asyrofa Rahmi ◽  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy ◽  
Andi Maulidinnawati A. K. Parewe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Learning Rate ◽  
Signature Recognition ◽  
Artificial Neural Network Ann ◽  
Number Of Iterations

The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.

scholarly journals Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 551 ◽  
Author(s):  
Baraka Mathew Nkurlu ◽  
Chuanbo Shen ◽  
Solomon Asante-Okyere ◽  
Alvin K. Mulashani ◽  
Jacqueline Chungu ◽  
...  
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Group Method ◽  
Well Testing ◽  
Well Log ◽  
Data Handling ◽  
Permeability Model ◽  
Log Data ◽  
Artificial Neural Network Ann

Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

scholarly journals Various Techniques Involving Plant Leaf Diseases Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 1544-1550
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Disease Classification ◽  
Biological Research ◽  
Support Vector ◽  
Agricultural Field ◽  
Plant Leaf ◽  
Artificial Neural Network Ann

In agricultural field, paddy development assumes an imperative job. Be that as it may, their developments are influenced by different diseases. There will be diminish in the plant growth, if the illnesses are not recognized at an early arrange. There are several image processing methods we can custom such as Genetic algorithm, Probabilistic Neural Network (NN), Back propagation Neural Network (BNN), Artificial-Neural-Network(ANN), and Support vector machine(SVM). Choosing an organization technique is continuously a tough task since the worth of outcome can differ for unlike input data. Plant leaf infection categorizations have wide-ranging applications in several fields such as in biological research, in Agriculture etc. This survey affords a summary of dissimilar organisation systems used for plant leaf disease classification. Also we have discoursed prevailing segmentation technique beside with classifiers for exposure of plant leaves.

Electricity Price and Load Short-Term Forecasting Using Artificial Neural Networks

2006 ◽  
Vol 7 (4) ◽  
Author(s):  
Paras Mandal ◽  
Tomonobu Senjyu ◽  
Naomitsu Urasaki ◽  
Toshihisa Funabashi
Keyword(s):  
Load Forecasting ◽  
Back Propagation ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Computing Technique ◽  
Ann Models ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Artificial Neural Network Ann

This paper presents an approach for short-term electricity price and load forecasting using the artificial neural network (ANN) computing technique. The described approach uses the three-layered ANN paradigm with back-propagation. The publicly available data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The ANN approach based on similarity technique has been proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of similar days. Two different ANN models, one for load forecasting and another for price forecasting, have been proposed. Test results show that average price and load MAPEs for the year 2003 by using the ANN approach are obtained as 14.29% and 0.95%, respectively. MAPE values obtained from the price and load forecasting results confirm considerable value of the ANN based approach in forecasting short-term electricity prices and loads.

scholarly journals Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

2022 ◽  
Vol 11 (02) ◽  
pp. 41-44
Author(s):  
Hamed Nazerian ◽  
Adel Shirazy ◽  
Aref Shirazi ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silicon Oxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Algorithm

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

scholarly journals An Integrated Access to Electricity Price Forecasting using K Means based ANN

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1677-1681
Keyword(s):  
Neural Network ◽  
Power Markets ◽  
Price Forecasting ◽  
Market Clearing ◽  
Electricity Price Forecasting ◽  
Time Period ◽  
Proposed Model ◽  
Trial Outcomes ◽  
Artificial Neural Network Ann ◽  
Absolute Percent Error

Mid-time period strength market Clearing charge (MCP) looking forward to is some days beforehand forecasts for each day facts. It has ended up being vital for better implementation of asset appropriation, making plans, respective contracting and arranging reasons for a strength exhibit. in this paper, an integrated midterm strength MCP estimating version is proposed to foresee the hourly MCPs for an entire month. The proposed model incorporates a k manner bunching module and artificial Neural network (ANN) guaging module. The ok way bunching module is applied to signify the 24 hours of multi day into some gatherings dependent on the closeness in cost. After the association, a Multi Layered Perceptron (MLP) is used to gauge the fee esteems in every one of the gatherings. to check the exactness of the proposed version the imply Absolute percent error (MAPE) and relapse coefficients are resolved for each one of the gatherings. Trial outcomes making use of recorded records from the Indian power Markets showed that the proposed included anticipating version can enhance the expectation exactness of price esteems and ultimately enhance the overall framework exhibitions.

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