Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach

Zhou, Ming; Yan, Zheng; Ni, Yixin; Li, G.; Nie, Yunpeng

doi:10.1049/ip-gtd:20045131

Cited by 100 publications

(50 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Apart from basic AR and ARMA specifications, a whole range of alternative models have been proposed. The list includes ARIMA and seasonal ARIMA models (Contreras et al, 2003, Zhou et al, 2006, autoregressions with heteroskedastic (Garcia et al, 2005) or heavy-tailed innovations, AR models with exogenous (fundamental) variables -'dynamic regression' (or ARX) and 'transfer function' (or ARMAX) models (Conejo et al, 2005), vector autoregressions with exogenous effects (Panagiotelis and Smith, 2008), threshold AR and ARX models (Misiorek et al, 2006), regime-switching regressions with fundamental variables (Karakatsani and Bunn, 2008) and mean-reverting jump diffusions (Knittel and Roberts, 2005).…”

Section: Introductionmentioning

confidence: 99%

Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

Weron

Misiorek²

2008

International Journal of Forecasting

317

214

View full text Add to dashboard Cite

This empirical paper compares the accuracy of 12 time series methods for short-term (dayahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models, their extensions -spike preprocessed, threshold and semiparametric autoregressions (i.e. AR models with nonparametric innovations), as well as, mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, while this is not necessarily the case when air temperature is considered as the exogenous variable, and that (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, more importantly, they have the potential to perform well under diverse market conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

Weron

Misiorek²

2008

International Journal of Forecasting

317

214

View full text Add to dashboard Cite

show abstract

“…where, G tst defined as (17) and suffix tst denote any testing or real time input pattern for which output is desired from a trained RBFNN. Output Y can be calculated using (16 …”

Section: Radial Basis Function Neural Network Model Based Modelmentioning

confidence: 99%

“…Many techniques and models have been developed for forecasting whole sale electricity prices, especially for short term price forecasting [3]. The state of art techniques for electricity price forecasting are categorized into equilibrium analysis [5], simulation methods [10], econometric methods [11], time series [12]- [14], intelligent systems [15]- [17] and volatility analysis [18]. Time series and intelligent systems are commonly used for day-ahead price forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

Singh¹,

Mohanty²

2015

JPEE

View full text Add to dashboard Cite

In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models; and 2) Simulation based models; time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.

show abstract

“…Statistical models like ARIMA and ARMA to forecast spot market prices are applied in [9], [10], [11]. GARCH processes are used to forecast spot market prices in [12].…”

Section: A Modeling Of Prices In Spot and Real-time Marketmentioning

confidence: 99%

Short-term hydropower planning with uncertain wind power production

Vardanyan

Amelin

Hesamzadeh

2013

2013 IEEE Power &Amp; Energy Society General Meeting

View full text Add to dashboard Cite

Abstract-The main purpose of this paper is to summarize the findings from simulating two stochastic short-term planning models for a price-taker hydropower producer.The first model is a two-stage stochastic linear programming problem. Profound sensitivity analysis is provided in terms of volatility in spot market prices and water inflow level. The results show that for the short-term hydropower planning problems the effect of considering price uncertainty in the stochastic model is higher compared to considering inflow level uncertainty.The second model is a two-stage stochastic linear programming problem. The model generates optimal bids to spot market considering real-time market price uncertainties. While simultaneously bidding to both markets, the results are not realistic. To make the bidding strategies more flexible and robust different approaches are modeled and assessed. Finally one of the approaches is suggested as the most applicable one.

show abstract

Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach

Cited by 100 publications

References 15 publications

Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

Short-term hydropower planning with uncertain wind power production

Contact Info

Product

Resources

About