Electricity price forecasting based on GARCH model in deregulated market

Zheng, Hairong; Xie, Lei; Zhang, Lizi

doi:10.1109/ipec.2005.206943

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…It is particularly beneficial when there are phases of fast-changing variation (or volatility). The GARCH (p,q) method is given in the following sections [10]: -…”

Section: ) Garch Modelmentioning

confidence: 99%

“…ES technique is used for EPF. The associated equations are accessible from Equation (10). In this paper, forecasting consists of three different smoothing levels that are at α = 0.2, α = 0.6, and at optimum alpha as shown in Figures 5-8.…”

Section: Scenario 1: Forecasting Using Esmentioning

confidence: 99%

“…H. Daneshi et al [4] gave a brief survey of different methodologies and their development in the field of EP forecasting. H. Zheng et al [10] represented forecasting the electricity price using GARCH. EP is considered an economic time series and analyzes its volatility, which illustrates the existence of heteroskedasticity.…”

Section: Introductionmentioning

confidence: 99%

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Nodal Electricity Price Forecasting using Exponential Smoothing and Holt’s Exponential Smoothing

Ahmed

Kumar

2023

DGAEJ

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The prediction of nodal electricity price (NEP) is a primary step to be done before the bidding process starts in the actual market environment. NEP plays a significant role for the efficient working of the electrical system. NEP follows a common trend as during peak hours when the load is high the price will also be high similarly during off-peak-load times the price will be lower and common to all the node. Thus, accurate forecasting of the NEP can help electricity generation companies to be more proactive in the wholesale electricity market to maximize its overall benefits. In this paper, exponential smoothing (ES), and holt’s exponential smoothing (HES) have been utilized for forecasting the NEP. Furthermore, a comparative analysis between ES and HES has been done considering several alpha values and several trends. The model evaluation and the forecasting performance have been tested using different parameters of ES, and HES techniques such as Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICc), Bayesian Information Criteria (BIC). The performance of the proposed technique has been authenticated efficaciously on average nodal real-time price data collected from ISO New England (BOSTON Zone).

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“…It is particularly beneficial when there are phases of fast-changing variation (or volatility). The GARCH (p,q) method is given in the following sections [10]: -…”

Section: ) Garch Modelmentioning

confidence: 99%

Section: Scenario 1: Forecasting Using Esmentioning

confidence: 99%

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Nodal Electricity Price Forecasting using Exponential Smoothing and Holt’s Exponential Smoothing

Ahmed

Kumar

2023

DGAEJ

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“…A number of statistical methodologies have been proposed for energy price and demand forecasting. Many approaches based on time series models have been used for price forecasting, such as AR models [1], autoregressive integrated moving average (ARIMA) models [2] [3] [4], and generalised autoregressive conditional heteroschedastic models (GARCH) [5]. Moreover, neural networks (NNs) are used widely for electricity price/demand forecasting in the literature [6], [7].…”

Section: Contextmentioning

confidence: 99%

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

Nguyen

Nabney

2010

Energy

117

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This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with …xed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman …lter or particle …lter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is signi…cantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

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“…Examples of time-series forecasting models are autoregressive integrated moving average (ARIMA), 4 Autoregressive Moving Average Exogenous, 5 and generalized autoregressive conditional heteroscedasticity (GARCH). 6,7 The electricity prices are frequently changed and not in a weekly pattern as a result of the problem in time-series techniques (AR, ARIMA, and GARCH). 8 Furthermore, sliding-window metaheuristic optimized machine-learning regression was proposed for future stock price 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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Electricity price forecasting based on GARCH model in deregulated market

Cited by 12 publications

References 8 publications

Nodal Electricity Price Forecasting using Exponential Smoothing and Holt’s Exponential Smoothing

Nodal Electricity Price Forecasting using Exponential Smoothing and Holt’s Exponential Smoothing

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

An electricity price interval forecasting by using residual neural network

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