Forecasting electricity spot-prices using linear univariate time-series models

By Jesús Crespo Cuaresma, Jaroslava Hlouskova, Stephan Kossmeier, Michael Obersteiner. 2004

362 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/b58965ff42e9d75b28312971d61cf891a8b15bc4

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In Electrical and Electronic Engineering

In Management Science and Operations Research

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices (2006, methodology): “[7], ARIMA models [2], [7]–[9], multivariate dynamic regression (DR) models [2], [10], multivariate transfer function (TF) models [2], [10], [11], and GARCH models [12] have been applied.”
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models (2008, methodology): “MRJD models have been also used for forecasting hourly electricity spot prices (Cuaresma et al., 2004, Knittel and Roberts, 2005) and volatility (Chan et al., 2008), though with moderate success.”
Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, neutral): “Examples of ARMA-type models for power markets include Cuaresma et al. (2004), who applied variants of AR(1) and general ARMA processes (including ARMA with jumps) to STPF in the German electricity market.”
Using extreme value theory to measure value-at-risk for daily electricity spot prices (2006, background): “An extensive discussion of VaR use in traditional financial markets can be found in Dowd (1998), Duffie and Pan (1997), Jorion (2000), Holton (2003) and Manganelli and Engle (2004), whilst energy VaR is detailed in Clewlow and Strickland (2000) and Eydeland and Wolyniec (2003).”
Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting (2012, methodology): “For this reason time series methods such as linear univariate model [9], ARIMA [3], Hagan and Behr [15], in combination with econometrics model [10], GIGARCH [12], GARCH [13] and hybrid models such as combination of ARIMA and GARCH using wavelet transform [28] were not considered for STMLF.”
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions (2013, background): “Huisman et al., 2006; Conejo et al., 2005; Cuaresma et al., 2004 ), (ii) by accounting for the price’s response to demand, fuel prices or weather conditions (e.g.”
Estimating the value of electricity storage in an energy-only wholesale market (2015, background): “Electricity has generally been considered a non-storable commodity [3].”
Electricity price forecasting in deregulated markets: A review and evaluation (2009, background): “[32] have demonstrated a comparison of the performance of 50 linear univariate time series AR and ARMA models.”
Mixed Models for Short-Run Forecasting of Electricity Prices: Application for the Spanish Market (2007, background): “Crespo-Cuaresma et al. [ 10 ] have suggested a group of univariate models to predict electricity prices in the Leipzig market, the most important spot market in Germany.”
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models (2005, methodology): “ARIMA models [1] and [3], dynamic regression models [2], other time series techniques [4] and [ 5 ], neural network procedures [6]–[13], wavelet transform models [14] and [15], heuristics [16], Bayesian techniques [17], and simulations and others [18]–[20].”
Feasibility of CHP-plants with thermal stores in the German spot market (2009, background): “Market mechanism failure and capacity constraints in the network may cause spikes, because they lead to temporary deviations from efficient competition in the market [27].”