Forecasting day-ahead price spikes for the Ontario electricity market

Sandhu, Harmanjot Singh; Fang, Liping; Guan, Ling

doi:10.1016/j.epsr.2016.08.005

Cited by 54 publications

(29 citation statements)

References 31 publications

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“…The forecasting performance is evaluated by means of some of the most utilised error metrics in the literature, e.g., [10], which are: mean absolute percentage error (MAPE), mean absolute error (MAE), and root-mean-square error (RMSE). These error measures for a certain period of time, N, are computed as follows:…”

Section: Model Performance Measures and Criteriamentioning

confidence: 99%

“…The literature on short-term (i.e., horizons ranging from one day to one week) forecasting models in electricity market contexts is mostly dominated by statistical or econometric approaches, whereas longer-term contexts also involve fundamental modelling of the market behaviour and dynamics [1][2][3]. Statistical and econometric approaches (e.g., time series such as ARIMA (autoregressive integrated moving average) and extensions [4][5][6][7], neural network and other AI (artificial intelligence) models [8][9][10][11], etc.) have received a considerable acceptance for many years due to their ability to capture linear (time series) and non-linear (AI) trends.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology

2019

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Various power exchanges are nowadays being affected by a plethora of factors that, as a whole, cause considerable instabilities in the system. As a result, traders and practitioners must constantly adapt their strategies and look for support for their decision-making when operating in the market. In many cases, this calls for suitable electricity price forecasting models that can account for relevant aspects for electricity price forecasting. Consequently, fundamental-econometric hybrid approaches have been developed by many authors in the literature, although these have rarely been applied in short-term contexts, where other considerations and issues must be addressed. Therefore, this work aims to develop a robust hybrid methodology that is capable of making the most of the advantages fundamental and the hybrid model in a synergistic manner, while also providing insight as to how well these models perform across the year. Several methods have been utilised in this work in order to modify the hybridisation approach and the input datasets for enhanced predictive accuracy. The performance of this proposal has been analysed in the real case study of the Iberian power exchange and has outperformed other well-recognised and traditional methods.

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Section: Model Performance Measures and Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology

2019

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“…Market price forecasting models for India are proposed in [6], using Model Confidence Set (MCS) approach to test the utility of these models and picking up the "best" models. Other domain-specific market price forecasting approach is proposed in [7], with focus on the identification of market price spikes in the Ontario electricity market. The work presented in [8], on the other hand, studies the influence of specificities of the market and weather conditions on the electricity price.…”

Section: Several Relevant Advances Have Been Accomplished In Forecastmentioning

confidence: 99%

Adaptive entropy-based learning with dynamic artificial neural network

2019

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Entropy models the added information associated to data uncertainty, proving that stochasticity is not purely random. This paper explores the potential improvement of machine learning methodologies through the incorporation of entropy analysis in the learning process. A multi-layer perceptron is applied to identify patterns in previous forecasting errors achieved by a machine learning methodology. The proposed learning approach is adaptive to the training data through a retraining process that includes only the most recent and relevant data, thus excluding misleading information from the training process. The learnt error patterns are then combined with the original forecasting results in order to improve forecasting accuracy, using the Rényi entropy to determine the amount in which the original forecasted value should be adapted considering the learnt error patterns. The proposed approach is combined with eleven different machine learning methodologies, and applied to the forecasting of electricity market prices using real data from the Iberian electricity market operator-OMIE. Results show that through the identification of patterns in the forecasting error, the proposed methodology is able to improve the learning algorithms' forecasting accuracy and reduce the variability of their forecasting errors.

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“…For instance, Panapakidis and Dagoumas [20] use the artificial neural networks (ANNs) model for electricity price forecasting in Southern Italy. Sandhu et al [21] employ the neural networks to forecast Ontario electricity prices. To better capture the characteristics of electricity prices, a combination of ANN models and other models is often presented.…”

Section: Introductionmentioning

confidence: 99%

An accurate analysis of the parameters affecting consumption and price fluctuations of Electricity in the Iranian market during Summer

et al. 2020

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In this paper, a novel method is proposed to predict the cost of short-term hourly electrical energy based on combined neural networks. In this method, the influential parameters that play a key role in the accuracy of these systems are identified and the most prominent ones are selected. In the proposed method, initially, using the SOM network, similar days are placed in close clusters. In the next stage, the temperature parameter and prices pertaining to similar days are trained separately in two MLP neural networks because of their differences concerning the range of changes and their nature. Finally, the two networks are merged with another MLP network. In the proposed hybrid method, an evolutionary search method is used to provide an appropriate initial weight for neural network training. Given the price data changes, the price amidst the previous hour has a significant effect on the prediction of the current state. In this vein, in the proposed method, the predicted data in the previous hour is considered as one of the inputs of the next stage. The proposed method was assessed on the datasets of Iran in the summer. This information pertains to the 2011-2016 period.

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Forecasting day-ahead price spikes for the Ontario electricity market

Cited by 54 publications

References 31 publications

Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology

Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology

Adaptive entropy-based learning with dynamic artificial neural network

An accurate analysis of the parameters affecting consumption and price fluctuations of Electricity in the Iranian market during Summer

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