scholarly journals (Application of Artificial Neural Networks Model for Forecasting Consumption of Electricity in Gezira State, Sudan (2006-2018: تطبيقات الشبكات العصبية الاصطناعية للتنبؤ باستهلاك الكهرباء في ولاية الجزيرة، السودان (2006-2018)

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Nada Mohammed Ahmed Alamin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Time Series Data ◽  
Electricity Consumption ◽  
Series Data ◽  
Control Center ◽  
Consumption Data ◽  
Artificial Neural ◽  
Quasi Newton ◽  
National Control

This paper aimed applying models of artificial neural networks to electricity consumption data in the Gezira state, Sudan for the period (Jan 2006- May 2018), and predicting future values for the period (Jun 2018- Dec 2020) by train a recurrent neural network using Quasi-Newton Sampling and using online learning. The study relied on data from the national control center. After applying artificial neural networks, The Thiel coefficient is used to confirm the efficiency of the model, and the paper recommends the use of artificial neural networks to various time series data due to their strength and Accuracy.

scholarly journals Deep Learning and Autoregressive Approach for Prediction of Time Series Data

2021 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Akhter Mohiuddin Rather
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Statistical Models ◽  
Time Complexity ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach ◽  
Proposed Model ◽  
Artificial Neural

Fractional This paper proposes a deep learning approach for prediction of nonstationary data. A new regression scheme has been used in the proposed model. Any non-stationary data can be used to test the efficiency of the proposed model, however in this work stock data has been used due to the fact that stock data has a property of being nonlinear or non-stationary in nature. Beside using proposed model, predictions were also obtained using some statistical models and artificial neural networks. Traditional statistical models did not yield any expected results; artificial neural networks resulted into high time complexity. Therefore, deep learning approach seemed to be the best method as of today in dealing with such problems wherein time complexity and excellent predictions are of concern.

scholarly journals Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Narayanan Manikandan ◽  
Srinivasan Subha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Stock Prices ◽  
Time Series Data ◽  
Financial Analysis ◽  
Parallel Implementation ◽  
Series Data ◽  
Artificial Neural

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

scholarly journals Prediction prices of basrah light oil using artificial neural networks

2020 ◽  
Vol 10 (3) ◽  
pp. 2682
Author(s):  
Maysaa Abd Ulkareem Naser
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Economic Activity ◽  
Global Economy ◽  
Time Series Data ◽  
Supply And Demand ◽  
Oil Prices ◽  
Developed Countries ◽  
Series Data ◽  
Artificial Neural

The global economy is assured to be very sensitive to the volatility of the oil market. The beneficial from oil prices collapse are both consumers and developed countries. Iraq economy is a one-sided economy which is completely depends on oil revenue to charge the economic activity. Hence, the current decline in oil prices will produce serious concerns. Some factors stopped most investment projects, rationalize the recurrent outflow, and decrease the development of economic activity. The study of forecast oil prices is considered among the most complex studies because of the different dynamic variables that affects the strategic goods. Moreover, the laws of economics controlling the prices of oil such as the supply and demand law. Some other variables that control the oil prices are the political conditions when these conditions contribute to the world production. The subject of forecasting has been extremely developing during recent years and some modern methods have been appeared in this regards, for example, Artificial Neural Networks. In this study, an artificial neural network (FFNN) is adopted to extract the complex relationships among divergent parameters that have the abilities to predict oil prices serving as an inputs to the network data collected in this research represent monthly time series data are Oil prices series in (US dollars) over a period of 11 years (2008–2018) in Iraq

scholarly journals Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 588
Author(s):  
Felipe Leite Coelho da Silva ◽  
Kleyton da Costa ◽  
Paulo Canas Rodrigues ◽  
Rodrigo Salas ◽  
Javier Linkolk López-Gonzales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Approach ◽  
Electricity Consumption ◽  
Industrial Sector ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
The One ◽  
Mlp Model

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

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