Prediction of length &amp; width of a rectangular patch antenna using ANN

Sarkar, Bishal Dey; Shankar, Sonali; Chaurasiya, Himanshu

doi:10.1109/indicon.2015.7443343

Cited by 1 publication

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, as the pattern of the power consumption data typically is not linear, it is preferable to utilise frameworks that can accommodate nonlinearities across variables (Kavaklioglu, 2011). It is common knowledge that artificial neural networks (ANN) can accurately mimic any nonlinear connection by varying the network configuration (Gallo et al., 2014; Mutascu, 2022; Sarkar, Shankar, & Chaurasiya, 2015; Sarkar, Shankar, Thakur, et al., 2015). Thus, the multilayer perceptron ANN approach is the main instrument in this study and is utilised to get reliable forecast results.…”

Section: Introductionmentioning

confidence: 99%

“…In several researches, a variety of forecasting techniques based on data mining were used to estimate future power or energy consumption. Multiple linear regression approach (Bianco et al., 2013; Ekonomou, 2010; Kialashaki & Reisel, 2014; Panklib et al., 2015), fuzzy logic (Ali et al., 2016; Islas et al., 2021; Kucukali & Baris, 2010; Olaru et al., 2022; Zahedi et al., 2013), autoregressive forecasting methods (Guefano et al., 2021; Kaytez, 2020; Nawaz et al., 2014; Nepal et al., 2020; Ozturk & Ozturk, 2018), support vector regression methods (Ekonomou, 2010; Hong & Fan, 2019; Kavaklioglu, 2011; Kaytez, 2020; Shao et al., 2020) and ANN (Ekonomou, 2010; Elbeltagi & Wefki, 2021; Günay, 2016; Kialashaki & Reisel, 2014; Li et al., 2018; Sarkar, Shankar, & Chaurasiya, 2015; Sarkar, Shankar, Thakur, et al., 2015; Torabi et al., 2019; Zahedi et al., 2013) have been extensively employed for this purpose. Several studies have been published over the past 10 years using a variety of approaches to anticipate the demand for electricity in various nations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling and forecasting India's electricity consumption using artificial neural networks

Bandyopadhyay,

Sarkar,

Hossain

et al. 2024

OPEC Energy Review

Self Cite

View full text Add to dashboard Cite

Precise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand.

show abstract