Spot electricity price forecasting in Indian electricity market using autoregressive-GARCH models

Girish, G P

doi:10.1016/j.esr.2016.06.005

Cited by 85 publications

(32 citation statements)

References 23 publications

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“…Other disciplines like in researches done by Girish (2016) had also included ARIMA-GARCH models, time series & price forecasting of electricity prices. The paper uses wave energy flux done by GARCH and including unconditional and conditional kernel density estimation, univariate and bivariate autoregressive moving average generalised autoregressive conditional heteroskedasticity (ARMA-GARCH) models and a regression-based method.…”

Section: Review Of Literaturementioning

confidence: 99%

Understanding volatility and leverage effects in bunker markets

Ranjan¹,

Raju

Jayaraj

et al. 2018

IJGEI

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The research analysed the volatility and the leverage effect in the bunker prices in IFO 380 and IFO 180 grades. Limited studies were found on bunker prices using EGARCH and bivariate analysis. The research investigated the causal relationships between IFO 380 and IFO 180 grades of bunkers. Serial correlations were found through using Lagrange multiplier (LM test) and heteroskedastic tests in the bunker price data. The analysis exhibited the model to be highly significant at 1%. Both the grades showed the short and long-run shocks and a positive leverage effect. The recommendations were made to the maritime industry to strategise during the long run and short-run shocks of bunker markets.

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Section: Review Of Literaturementioning

confidence: 99%

Understanding volatility and leverage effects in bunker markets

Ranjan¹,

Raju

Jayaraj

et al. 2018

IJGEI

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“…In addition, hybrid forecasting models based on time series and non-time series models have developed in recent works. The hybrid time-series models were proposed, such as autoregressive-GARCH 16 and wavelet-ARIMA. 17 Furthermore, ANN with empirical mode decomposition (EMD) has implemented for the short-term locational marginal price (LMP) forecasting.…”

Section: Introductionmentioning

confidence: 99%

An electricity price interval forecasting by using residual neural network

Chaweewat

Singh

2020

Int Trans Electr Energ Syst

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Summary This article proposed a new electricity price interval forecasting method based on a novel Residual Neural Network (ResNet) for the electricity price interval forecasting. The significant outcome of the ResNet model was that the model performs excellently on normal and spike price interval forecasting in accuracy and reliability point of view. The proposed ResNet was consisting of two network layers. The first neural network layers were probabilistic normal, high, and spike prices prediction part. The Lower and Upper Bound Estimation (LUBE) formulates the price interval forecasting from the output of the second neural network layers. The LUBE methods included Quantile Regression and Mean and Variance estimation. The proposed forecasting models were demonstrated with the GEFCom2014 dataset. The dataset is consisting of 15 tasks for electricity prices forecasting. The results of proposed ResNet models compared with GEFCom2014's benchmarks, Quantile Regression Average, and Multilayer Perceptron Network approaches. The performances of forecasting models are evaluated in terms of accuracy and reliability metrics by Pinball Loss Function and Coverage Width‐based Criterion (CWC), respectively. Also, this article shows the effect of an increase in confidence level, which could generate lower CWC values and represent high reliability's satisfaction.

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“…Spot electricity prices are further more volatile than any other commodity and are known for extreme price volatility [9]- [15]. Electricity price series exhibit certain stylized facts namely Seasonality, Mean Reversion, Volatility and Jumps/Spikes [2] [16]. Being a case, it makes it extremely relevant for modeling spot electricity prices having lead time of few hours/days.…”

Section: Introductionmentioning

confidence: 99%

“…Southern region was connected to Central Grid for power transmission on 31 st December 2013, thereby achieving "One Nation-One Grid-One Frequency". Other studies in literature by Ghosh and Kanjilal [18], Girish and Tiwari [19] and Girish [2] focused on forecasting Indian spot electricity prices using data at a time when different regions of Indian electricity market were not interconnected. With growing number of power market participants/open access consumers whose numbers have surpassed 3530 in Indian Energy Exchange, the results of our study are proposed to help these power market participants strategically plan their exposure in day-ahead Indian spot electricity market.…”

Section: Introductionmentioning

confidence: 99%

Does Indian Spot Electricity Price Series Exhibit Inverse-Leverage Effect?

Girish¹,

Vijayalakshmi²

2018

TEL

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In this study, we investigate whether Indian Spot Electricity Price Series exhibit Inverse-Leverage effect by modeling Indian spot electricity price using ARIMA-EGARCH model using data given by Indian Energy Exchange from January 1 st 2014 to 31 st December 2015 accounting for 730 days and 17,520 hourly spot prices for all the five regions of Indian electricity market. Conditional mean and conditional variance equations are estimated. The results of the study provide crucial insights for power market participants/open access consumers whose numbers have surpassed 3530 in Indian Energy Exchange to strategically plan their exposure in Indian spot electricity market.

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Spot electricity price forecasting in Indian electricity market using autoregressive-GARCH models

Cited by 85 publications

References 23 publications

Understanding volatility and leverage effects in bunker markets

Understanding volatility and leverage effects in bunker markets

An electricity price interval forecasting by using residual neural network

Does Indian Spot Electricity Price Series Exhibit Inverse-Leverage Effect?

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