An Application of NGBM for Forecasting Indian Electricity Power Generation

Singh, Dushant P.; Gadakh, Prashant; Dhanrao, Pravin M.; Mohanty, Sharmila; Swain, Debabala; Swain, Debabrata

doi:10.1007/978-981-10-3874-7_20

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…23 What is more, grey theory based on nonlinear grey Bernoulli model (NGBM) and Markov model is also applied to forecast the average EP. 24,25 ARIMA is used to forecast up to 2053 by applying 40 years of past data from 1973 to 2013. By experimental comparison, ARIMA(0,2) demonstrated a better prediction based on accuracy during the holdback period.…”

Section: Related Research Workmentioning

confidence: 99%

“…Two evaluating indexes are used to exhibit the modeling error accuracy and the results indicate that the novel model could furnish the forecasting with more freedom 23 . What is more, grey theory based on nonlinear grey Bernoulli model (NGBM) and Markov model is also applied to forecast the average EP 24,25 . ARIMA is used to forecast up to 2053 by applying 40 years of past data from 1973 to 2013.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fastest‐growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

Qiao¹,

Li²,

Liu

et al. 2021

Intl J of Energy Research

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Electricity is an important indicator for economic development, especially electricity production (EP), which is electricity industry managers making strategic decisions. There are many ways to produce electricity, which is the source of rapid growth in EP is rarely studied. Due to the nonstationary and nonlinearity of the EP time series, traditional methods are less robust to predict it. In this study, a novel combination prediction model is proposed based on wavelet transform (WT), long short-term memory (LSTM), and stacked autoencoder (SAE). Comparisons between the SAE-LSTM and the advanced prediction model. We compared SAE-LSTM and the advanced prediction model including BP (Back Propagation) etc. In addition, the performance comparison of the different wavelet layers based on SAE-LSTM and the performance comparison of the EMD and EEMD based on SAE-LSTM are also compared. At last, future average growth rates (June 2021 ! December 2022) are predicted. The empirical result shows that the combination model in view of SAE-LSTM exceeds the benchmark models. The results also imply that WT-SAE-LSTM outperforms the EMD, EEMD-SAE-LSTM, and SAE. Based on the optimal orders and layers of Coiflets combining with SAE-LSTM, natural gas is the fastest-growing source of EP in the United States.

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Section: Related Research Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fastest‐growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

Qiao¹,

Li²,

Liu

et al. 2021

Intl J of Energy Research

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show abstract

“…In 2017, Dushant P.Singh et al used the nonlinear grey Bernoulli model (NGBM) [12], which adjusted the stability of GM (1,1) appropriately with Bernoulli differential equation (BDE) [13]. Researchers compared the accuracy of the nonlinear grey Bernoulli (NGBM) model and the grey GM model with PRE and ARPE values, concluding that prediction results from NGBM (1,1) were more accurate than GM (1,1).…”

Section: Electric Power Research In Indiamentioning

confidence: 99%

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Zhou

2019

Energies

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With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

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