Energy price forecasting in the Ontario competitive power system market

By C. P. Rodriguez, George J. Anders. IEEE Transactions on Power Systems, 2004

351 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/d6992f10458e40769d982ec27d9021c560ee8a86

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In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques (2007, methodology): “The methodology described above has been applied to the working days of 2002 (four atypical days of the last week of December, much closer to a holiday than to a working day, have also been excluded from the analysis).”
Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, neutral): “While there is a variety of empirical studies on evaluating point forecasts in electricity markets (Szkuta et al., 1999, Nogales et al., 2002, Contreras et al., 2003, Rodriguez and Anders, 2004, Weron and Misiorek, 2005), to the best of our knowledge density or interval forecasts have not been inv”
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method (2007, methodology): “Another variable that drives the price is the hour during the day; however, its impact is also reﬂected in the demand [14].”
Mixed Models for Short-Run Forecasting of Electricity Prices: Application for the Spanish Market (2007, neutral): “[5].”
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models (2005, methodology): “…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 others [18]–[20].”
An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets (2008, methodology): “also been applied for energy price forecasting in the Ontario power market [14].”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “…for the Australian (Amjady&Keynia, 2009; Becker, Hurn, & Pavlov, 2007; Christensen, Hurn, & Lindsay, 2012; Dong, Wang, Jiang, & Wu, 2011) and Ontario (Aggarwal, Saini, & Kumar, 2008; Lei & Feng, 2012; Mandal, Haque, Meng, Martinez, & Srivastava, 2012; Rodriguez & Anders, 2004) power markets.”
Combining hydro-generation and wind energy: Biddings and operation on electricity spot markets (2007, background): “In order to calculate the expected penalty it is necessary to forecast the day ahead energy prices [2–4].”
Economic Impact of Electricity Market Price Forecasting Errors: A Demand-Side Analysis (2010, neutral): “For example, forecast errors ranging from about 5% to 20% were reported for the Spanish [2], PJM [6], and Ontario [7], [8] electricity markets.”
Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices (2006, methodology): “Furthermore, the 1-hour-ahead HOEP forecasts presented in [5] by using a non-linear neuro-fuzzy model have daily MAPEs varying between 19.”
Day-ahead price forecasting of electricity markets by a new fuzzy neural network (2006, background): “Rodriguez and Anders compared an NN owning two learning algorithms with a neuro-fuzzy system for forecasting of hourly MCP in the Ontario energy market, where the later showed better prediction accuracy than the former [15].”
Electricity price forecasting in deregulated markets: A review and evaluation (2009, methodology): “[48] [(1,2)-(4,8,12)-(1)] S/L, FMF/L ANN – 13 to 49,”