Forecasting Next-Day Electricity Prices by Time Series Models

By Francisco Javier Nogales, Javier Contreras, Antonio J. Conejo, Rosa Espínola. IEEE Power Engineering Review, 2002

933 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/672dab27d709a863eb50adb67c7e55b913e31ded

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In Electrical and Electronic Engineering

Citations in the corpus

Price-Taker Bidding Strategy under Price Uncertainty (2002, methodology): “The third column shows the estimates of the energy prices using the forecasting method proposed in [32] with a level of confidence equal to 99%.”
Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques (2007, neutral): “This category comprises a diversity of methods ranging from the simplest “black box” time-series methods, using only previous price as input data, to more complex structural forecasting models that include explanatory (causal) variables such as load demand, fuel prices, and generation availability.”
ARIMA Models to Predict Next-Day Electricity Prices (2002, background): “Stochastic models of prices, as in [14], are also competing with traditional time series models in order to predict daily or average weekly prices [15].”
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method (2007, methodology): “The PJM market that operates the largest competitive wholesale electricity market in the world is coordinated by an independent system operator (ISO) who ensure a secured, economical and efﬁcient operation as well as determining all locational marginal price (LMP) according to voluntary bids and…”
Risk-constrained self-scheduling of a thermal power producer (2004, methodology): “Price estimates are obtained using a transfer function procedure [19], while the covariance matrix has been estimated through”
Probabilistic forecasts of the magnitude and timing of peak electricity demand (2005, background): “From an operational point of view, the key question is whether there will be problems in meeting the peak demand; failure to meet this peak demand could result in blackouts.”
Modeling and forecasting electricity prices with input/output hidden Markov models (2005, methodology): “However, several stationary models have been applied in the literature to model electricity price time series. In [ 8 ], two models are presented.”
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…”
Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities (2013, positive): “Related to electricity price forecasting we can quote the papers by Conejo et al. (2005), Nogales et al. (2002) and Contreras et al. (2003), who computed one-day-ahead forecasts in several European and North-American Markets.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “An alternative, more robust to outliers variant of OLS averaging is proposed by Nowotarski et al. (2013a). They apply the absolute loss function instead of the quadratic one in eqn.”
Indian Stock Market Prediction Using Differential Evolutionary Neural Network Model (2012, background): “Financial time series[1][2][3][4] is highly noisy, irregular, random, non-linear, non-seasonal and chaotic in nature.”
Hourly Electricity Prices in Day-Ahead Markets (2007, positive): “Nogales et al. (2002) use price and demand load data obtained from the Spanish and U.S. markets to forecast next-day electricity prices by time series models.”