ARIMA Models to Predict Next-Day Electricity Prices

By Javier Contreras, Rosa Espínola, Francisco Javier Nogales, Antonio J. Conejo. 2002

1,755 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

MPC-Based Appliance Scheduling for Residential Building Energy Management Controller (2013, background): “Extensive studies about electricity price forecasting can be found in [14], [15], etc.”
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, methodology): “Statistical-based models, such as the Box–Jenkins model called ARIMA, were for many years the state-of-the-art for electricity time series forecasting [1,2].”
An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets (2008, background): “As seen from the WMAPE values in Table II, the prediction based on AWNN model is better compared to the MLP, RBF, wavelet-ARIMA and FNN models for all the test cases.”
Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, neutral): “(2002) and Contreras et al. (2003) fitted and evaluated transfer function (TF), dynamic regression (DR) and ARIMA (also with explanatory variables) models on exactly the same out-of-sample test period.”
Offering Strategy Via Robust Optimization (2011, methodology): “These bounds correspond to the energy prices of the Spanish area of the electricity market of the Iberian Peninsula on Monday, November 23, 2009 [1], which are obtained using the ARIMA method proposed in [26] with a level of confidence equal to 99%.”
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models (2005, methodology): “Reported techniques to forecast day-ahead prices include 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…”
A Stochastic Programming Approach to Electric Energy Procurement for Large Consumers (2007, background): “Uncertainty of pool prices is handled by treating them as stochastic variables that are characterized by an ARIMA model [14], [15].”
Day-ahead price forecasting of electricity markets by a new fuzzy neural network (2006, methodology): “as the cross-regression part [4]–[8], [11], [12], [17].”
Flexible Transmission Expansion Planning With Uncertainties in an Electricity Market (2009, background): “A number of forecasting techniques have been presented in the literature [14]–[17].”