Price forecasting and validation in the Spanish electricity market using forecasts as input data

Ortiz, M.C.; Ukar, Olatz; Azevedo, Filipe; Mugica, Arantza

doi:10.1016/j.ijepes.2015.11.004

Cited by 22 publications

(6 citation statements)

References 16 publications

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“…It is a vital commodity and no day could be conceived without it. One of the biggest difficulties when talking about electricity management is that there is no way to store large quantities of it and, consequently, a constant balance between demand and supply is necessary, heading to highly volatile market prices [1].…”

Section: Introductionmentioning

confidence: 99%

Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

et al. 2021

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The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the models.

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Section: Introductionmentioning

confidence: 99%

Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

et al. 2021

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“…Research related to energy prices is of great importance to the whole community from an economic and political point of view. Moreover, the prediction of electricity prices is crucial for participants in the electricity market to optimize profitability and improve and strengthen risk management [8,9]. An accurate price prediction is important for the electricity market and the entire power system.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

et al. 2021

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Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

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“…To better capture the characteristics of electricity prices, a combination of ANN models and other models is often presented. For instance, Ortiz et al [22] propose a combined model based on artificial neural networks. Keles et al [23] develop a model based on ANNs and optimal parameter model.…”

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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Price forecasting and validation in the Spanish electricity market using forecasts as input data

Cited by 22 publications

References 16 publications

Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

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

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