Electricity price forecasting in deregulated markets: A review and evaluation

Aggarwal, Shubhani; Saini, Lalit Mohan; Kumar, Ashwani

doi:10.1016/j.ijepes.2008.09.003

Cited by 448 publications

(258 citation statements)

References 39 publications

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“…This is very similar in many other domains where 1 SQLScript is a procedural programming language in SAP HANA. 2 L is a programming language similar to C used in SAP HANA.…”

Section: Discussionsupporting

confidence: 57%

“…Furthermore, several data-management and data-mining techniques for time series, such as indexing, clustering and classification [19], provide means for compressed time-series representation (Section 2.2). Another topic, time-series forecasting (Section 2.3), has gained much attention from the research community [1,7,33]. As time-series forecasting is very important in smart electricity grids [8], we use it as one important scenario in our evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…In recent research, an impressive number of forecasting techniques has been developed particularly for energy demand [1,7,33]. The author in [40] shows that the so-called exponential smoothing technique behaves particularly well in the case of short-term energy demand forecasting.…”

Section: Time-series Forecastingmentioning

confidence: 99%

“…However, while smart meters typically can generate data in high temporal resolution (e.g., measurements are recorded every second), frequently only measurements in a relatively low resolution (e.g., every 15 minutes) are being transmitted and used. At the same time, high-resolution data would be extremely beneficial for many analytical applications such as detailed visualization [30], energy disaggregation [21], monitoring of residential power quality [15], short-term forecasts of energy generation/load [7,33] or energy prices [1]. The explanation for not working with high-resolution data is the enormous amount of storage space required.…”

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confidence: 99%

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A time-series compression technique and its application to the smart grid

Eichinger

Efros

Karnouskos³

et al. 2014

The VLDB Journal

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Time-series data is increasingly collected in many domains. One example is the smart electricity infrastructure, which generates huge volumes of such data from sources such as smart electricity meters. Although today this data is used for visualization and billing in mostly 15-min resolution, its original temporal resolution frequently is more finegrained, e.g., seconds. This is useful for various analytical applications such as short-term forecasting, disaggregation and visualization. However, transmitting and storing huge amounts of such fine-grained data is prohibitively expensive in terms of storage space in many cases. In this article, we present a compression technique based on piecewise regression and two methods which describe the performance of the compression. Although our technique is a general approach for time-series compression, smart grids serve as our running example and as our evaluation scenario. Depending on the data and the use-case scenario, the technique compresses data by ratios of up to factor 5,000 while maintaining its usefulness for analytics. The proposed technique has outperformed related work and has been applied to three real-world energy datasets in different scenarios. Finally, we show that the proposed compression technique can be implemented in a state-of-the-art database management system.

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“…This is very similar in many other domains where 1 SQLScript is a procedural programming language in SAP HANA. 2 L is a programming language similar to C used in SAP HANA.…”

Section: Discussionsupporting

confidence: 57%

Section: Related Workmentioning

confidence: 99%

Section: Time-series Forecastingmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A time-series compression technique and its application to the smart grid

Eichinger

Efros

Karnouskos³

et al. 2014

The VLDB Journal

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“…Designing dynamic consumption requires forecasts of electricity sector. They showed ho transfer function model, the autor network technique, the kernel-base ɛ-insensitive loss function support parametric additive model, can be in electricity price forecasting have techniques have been covered: stoc models [6].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

et al. 2018

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Abstract:The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants' production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model.

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Prediction of electricity prices for non‐regulated markets based on a power transformed mean reverting process

Castañeda-Leyva¹,

Hernández‐Ramos

Pérez‐Hernández³

et al. 2022

Appl Stoch Models Bus & Ind

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The electricity price time series for non‐regulated markets presents two basic properties: (a) a mean reversion trend around a constant or deterministic function and (b) a price process with high volatility and marginal, conditional, and asymptotic distributions skewed to the right. We propose a microeconomic‐based model for the dynamics of electricity prices that provides a satisfactory explanation of the stylized features observed in non‐regulated electricity markets. The suggested model is based on a power transformation of the Ornstein–Uhlenbeck process, which accounts for the unobserved demand process. This means that the model requires only the spot price of the electricity price to be available. Parameter estimates were obtained using the maximum likelihood method. This approach was implemented for data from the Alberta electricity market in Canada, with satisfactory results in terms of residual analysis and forecasting. The success rate of predicting the price jump sign (upward and downward) was approximately 80%$$ 80\% $$. Moreover, the statistical properties for the maximum likelihood parameter estimators are shown via a Monte Carlo simulation of the electricity price time series.

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Electricity price forecasting in deregulated markets: A review and evaluation

Cited by 448 publications

References 39 publications

A time-series compression technique and its application to the smart grid

A time-series compression technique and its application to the smart grid

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

Prediction of electricity prices for non‐regulated markets based on a power transformed mean reverting process

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