Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

By Antonio J. Conejo, M.A. Plazas, Rosa Espínola, A. B. Molina. IEEE Transactions on Power Systems, 2005

1,012 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ec2ee29e3c7d01224058ca7fcc7bd7d5ce63f9bc

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques (2007, methodology): “Frequently, but not necessarily, leads to forecasting errors that are close to that of the optimal value.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “Conejo, Contreras et al. (2005), Contreras et al. (2003), Nogales et al. (2002), Shahidehpour et al. (2002) and Weron and Misiorek (2008).”
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks (2016, methodology): “In the domain of statistical forecasting techniques, [21] use wavelets and an ARIMA technique for forecasting.”
Economic Impact of Electricity Market Price Forecasting Errors: A Demand-Side Analysis (2010, positive): “For example, in [10], wavelet transforms were employed to improve the accuracy of an ARIMA model by about 2.”
Short-term wind power forecasting in Portugal by neural networks and wavelet transform (2011, background): “The smaller this variance, the more precise is the prediction [25].”
Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, background): “Forecasting models that are popularly applied to electrical load and the renewable energy forecasting can be classified into three categories: 1) statistical models, such as auroregressive (AR), AR integrated moving average (ARIMA), and exponential smoothing (ES) models [1], [3], [4]; 2) artificial ”
Modeling Real-Time Balancing Power Market Prices Using Combined SARIMA and Markov Processes (2008, methodology): “Previously, models based on such processes have been used for load forecasts [5]–[7] and to forecast day-ahead spot market prices [8]–[12].”
Electricity price forecasting using sale and purchase curves: The X-Model (2015, methodology): “The wavelet based approached is basically the popular wavelet-ARIMA model introduced by Conejo et al. (2005).”
Electricity price forecasting in deregulated markets: A review and evaluation (2009, methodology): “Persistence method [17,40] and naïve method [35] are some of the reported methods, which can be used as benchmark for testing the effectiveness of any new forecasting methodology.”
Using wavelets for time series forecasting: Does it pay off? (2010, methodology): “Conejo et al. (2005) decompose the time series into a sum of processes with different frequencies, and forecast the individual time series before adding up the results.”
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model (2017, background): “Its specific form can be expressed as ARIMA (p, d, q), where ‘p’ represents the order of autoregressive processes, ‘d’ represents the order of difference, and ‘q’ represents the order of the moving average process [45,46].”
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method (2007, methodology): “For example, time-series [7], ARIMA model [11], GARCH model [2], artiﬁcial neural networks (ANN) [5], [10], [12]–[14], wavelet transform and ARIMA models [23], agents-based simulations [16], input-output hidden Markov models (IOHMM) [15], fuzzy neural network [24], etc.”
Crude oil price analysis and forecasting using wavelet decomposed ensemble model (2012, methodology): “[40] combined wavelet analysis and ARMA model to analyze the Spanish electricity market and obtained positive results [40].”
An Optimal Power Scheduling Method for Demand Response in Home Energy Management System (2013, methodology): “In this section, an optimal approach for scheduling the power usage of all AOAs in the home is proposed based on the RTP combined with IBR pricing scheme.”