4,084 research outputs found

    On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market

    Full text link
    [EN] The mainstream of EU policies is heading towards the conversion of the nowadays electricity consumer into the future electricity prosumer (producer and consumer) in markets in which the production of electricity will be more local, renewable and economically efficient. One key component of a local short-term and medium-term planning tool to enable actors to efficiently interact in the electric pool markets is the ability to predict and decide on forecast prices. Given the progressively more important role of renewable production in local markets, we analyze the influence of renewable energy production on the electricity price in the Iberian market through historical records. The dependencies discovered in this analysis will serve to identify the forecasts to use as explanatory variables for an electricity price forecasting model based on recurrent neural networks. The results will show the wide impact of using forecasted renewable energy production in the price forecasting.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. D. Aineto is partially supported by the FPU16/03184.Aineto, D.; Iranzo-Sánchez, J.; Lemus Zúñiga, LG.; Onaindia De La Rivaherrera, E.; Urchueguía Schölzel, JF. (2019). On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market. Energies. 12(11):1-20. https://doi.org/10.3390/en121120821201211Conference of the Parties, Framework Convention on Climate Change, U.N. Adoption of the Paris Agreementhttps://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdfEuropean Commission 2018—Vision for a Long-Term EU Strategy for Reducting Greenhouse Gas Emissionshttps://ec.europa.eu/clima/policies/strategies/2050_en#tab-0-1Common Vision for the Renewable Heating and Cooling Sector in Europe: 2020–2030–2050 of the Renewable Heating and Cooling Technology and Innovation Platformhttp://www.rhc-platform.org/publications/Operador del Mercado Ibérico de Energía—Polo Español—Resultados de mercadohttp://www.omie.es/aplicaciones/datosftp/datosftp.jsp?path=García-Martos, C., Caro, E., & Jesús Sánchez, M. (2015). Electricity price forecasting accounting for renewable energies: optimal combined forecasts. Journal of the Operational Research Society, 66(5), 871-884. doi:10.1057/jors.2013.177Grossi, L., & Nan, F. (2019). Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources. Technological Forecasting and Social Change, 141, 305-318. doi:10.1016/j.techfore.2019.01.006Madani, K., & Lund, J. R. (2009). Estimated impacts of climate warming on California’s high-elevation hydropower. Climatic Change, 102(3-4), 521-538. doi:10.1007/s10584-009-9750-8Moemken, J., Reyers, M., Feldmann, H., & Pinto, J. G. (2018). Future Changes of Wind Speed and Wind Energy Potentials in EURO-CORDEX Ensemble Simulations. Journal of Geophysical Research: Atmospheres, 123(12), 6373-6389. doi:10.1029/2018jd028473Jerez, S., Tobin, I., Vautard, R., Montávez, J. P., López-Romero, J. M., Thais, F., … Wild, M. (2015). The impact of climate change on photovoltaic power generation in Europe. Nature Communications, 6(1). doi:10.1038/ncomms10014Martiradonna, L. (2016). Robust against climate change. Nature Materials, 15(2), 127-127. doi:10.1038/nmat4559Mideksa, T. K., & Kallbekken, S. (2010). The impact of climate change on the electricity market: A review. Energy Policy, 38(7), 3579-3585. doi:10.1016/j.enpol.2010.02.035Golombek, R., Kittelsen, S. A. C., & Haddeland, I. (2011). Climate change: impacts on electricity markets in Western Europe. Climatic Change, 113(2), 357-370. doi:10.1007/s10584-011-0348-6Giulietti, M., Grossi, L., Trujillo Baute, E., & Waterson, M. (2018). Analyzing the Potential Economic Value of Energy Storage. The Energy Journal, 39(01). doi:10.5547/01956574.39.si1.mgiuBorenstein, S. (2012). The Private and Public Economics of Renewable Electricity Generation. Journal of Economic Perspectives, 26(1), 67-92. doi:10.1257/jep.26.1.67Aggarwal, S. K., Saini, L. M., & Kumar, A. (2009). Electricity price forecasting in deregulated markets: A review and evaluation. International Journal of Electrical Power & Energy Systems, 31(1), 13-22. doi:10.1016/j.ijepes.2008.09.003Notton, G., Nivet, M.-L., Voyant, C., Paoli, C., Darras, C., Motte, F., & Fouilloy, A. (2018). Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting. Renewable and Sustainable Energy Reviews, 87, 96-105. doi:10.1016/j.rser.2018.02.007Woo, C. K., Horowitz, I., Moore, J., & Pacheco, A. (2011). The impact of wind generation on the electricity spot-market price level and variance: The Texas experience. Energy Policy, 39(7), 3939-3944. doi:10.1016/j.enpol.2011.03.084Brancucci Martinez-Anido, C., Brinkman, G., & Hodge, B.-M. (2016). The impact of wind power on electricity prices. Renewable Energy, 94, 474-487. doi:10.1016/j.renene.2016.03.053Paraschiv, F., Erni, D., & Pietsch, R. (2014). The impact of renewable energies on EEX day-ahead electricity prices. Energy Policy, 73, 196-210. doi:10.1016/j.enpol.2014.05.004Milstein, I., & Tishler, A. (2015). Can price volatility enhance market power? The case of renewable technologies in competitive electricity markets. Resource and Energy Economics, 41, 70-90. doi:10.1016/j.reseneeco.2015.04.001Mulder, M., & Scholtens, B. (2013). The impact of renewable energy on electricity prices in the Netherlands. Renewable Energy, 57, 94-100. doi:10.1016/j.renene.2013.01.025Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. doi:10.1016/j.ijforecast.2014.08.008Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. doi:10.1109/tpwrs.2002.804943Crespo Cuaresma, J., Hlouskova, J., Kossmeier, S., & Obersteiner, M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87-106. doi:10.1016/s0306-2619(03)00096-5Conejo, A. J., Contreras, J., Espínola, R., & Plazas, M. A. (2005). Forecasting electricity prices for a day-ahead pool-based electric energy market. International Journal of Forecasting, 21(3), 435-462. doi:10.1016/j.ijforecast.2004.12.005Misiorek, A., Trueck, S., & Weron, R. (2006). Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models. Studies in Nonlinear Dynamics & Econometrics, 10(3). doi:10.2202/1558-3708.1362Garcia, R. C., Contreras, J., vanAkkeren, M., & Garcia, J. B. C. (2005). A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices. IEEE Transactions on Power Systems, 20(2), 867-874. doi:10.1109/tpwrs.2005.846044Catalão, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F., & Ferreira, L. A. F. M. (2007). Short-term electricity prices forecasting in a competitive market: A neural network approach. Electric Power Systems Research, 77(10), 1297-1304. doi:10.1016/j.epsr.2006.09.022Monteiro, C., Fernandez-Jimenez, L., & Ramirez-Rosado, I. (2015). Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market. Energies, 8(9), 10464-10486. doi:10.3390/en80910464González, C., Mira‐McWilliams, J., & Juárez, I. (2015). Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests. IET Generation, Transmission & Distribution, 9(11), 1120-1128. doi:10.1049/iet-gtd.2014.0655Anbazhagan, S., & Kumarappan, N. (2013). Day-Ahead Deregulated Electricity Market Price Forecasting Using Recurrent Neural Network. IEEE Systems Journal, 7(4), 866-872. doi:10.1109/jsyst.2012.2225733Sharma, V., & Srinivasan, D. (2013). A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market. Engineering Applications of Artificial Intelligence, 26(5-6), 1562-1574. doi:10.1016/j.engappai.2012.12.012Kuo, P.-H., & Huang, C.-J. (2018). An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks. Sustainability, 10(4), 1280. doi:10.3390/su10041280Pórtoles, J., González, C., & Moguerza, J. (2018). Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach. Energies, 11(6), 1588. doi:10.3390/en11061588EUPHEMIA Public Description - PCR Market Coupling Algorithmhttp://m.omie.es/files/16_11_28_Euphemia%20Public%20Description.pdf?m=yesReal Decreto 1578/2008, de 26 de Septiembre, de Retribución de la Actividad de Producción de Energía Eléctrica Mediante Tecnología Solar Fotovoltaica para Instalaciones Posteriores a la Fecha Límite de Mantenimiento de la Retribución del Real Decreto 661/2007, de 25 de mayo, para Dicha Tecnologíahttps://www.boe.es/boe/dias/2008/09/27/pdfs/A39117-39125.pdfReal Decreto 244/2019, de 5 de abril, por el que se Regulan las Condiciones Administrativas, Técnicas y Económicas del Autoconsumo de Energía Eléctricahttps://www.boe.es/boe/dias/2019/04/06/pdfs/BOE-A-2019-5089.pd

    Forecasting day-ahead electricity prices in Europe: the importance of considering market integration

    Full text link
    Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features

    Short-term electricity prices forecasting in a competitive market: A neural network approach

    Get PDF
    This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California

    A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

    Get PDF
    Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

    Development of Neurofuzzy Architectures for Electricity Price Forecasting

    Get PDF
    In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

    Day-Ahead Price Forecasting for the Spanish Electricity Market

    Get PDF
    During the last years, electrical systems around the world and in particular the Spanish electric sector have undergone great changes with the focus of turning them into more liberalized and competitive markets. For this reason, in many countries like Spain have appeared electric markets where producers sell and electricity retailers buy the power we consume. All agents involved in this market need predictions of generation, demand and especially prices to be able to participate in them in a more efficient way, obtaining a greater profit. The present work is focused on the context of development of a tool that allows to predict the price of electricity for the next day in the most precise way possible. For such target, this document analyzes the electric market to understand how prices are calculated and who are the agents that can make prices vary. Traditional proposals in the literature range from the use of Game Theory to the use of Machine Learning, Time Series Analysis or Simulation Models. In this work we analyze a normalization of the target variable due to a strong seasonal component in an hourly and daily way to later benchmark several models of Machine Learning: Ridge Regression, K-Nearest Neighbors, Support Vector Machines, Neural Networks and Random Forest. After observing that the best model is Random Forest, a discussion has been carried out on the appropriateness of the normalization for this algorithm. From this analysis it is obtained that the model that gives the best results has been Random Forest without applying the normalization function. This is due to the loss of the close relationship between the objective variable and the electric demand, obtaining an Average Absolute Error of 3.92€ for the whole period of 2016

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

    Get PDF
    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 modelMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277

    Some New Approaches to Forecasting the Price of Electricity: A Study of Californian Market

    Get PDF
    In this paper we consider the forecasting performance of a range of semi- and non- parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and time- and price-dependent volatility. The typical approach in this area has been to use a range of tools that have proven popular in the financial econometrics literature, where volatility clustering is common. However, electricity time series tend to exhibit higher volatility on a daily basis, but within a mean reverting framework, albeit with occasional large ’spikes’. In this paper we compare the existing forecasting performance of some popular parametric methods, notably GARCH AR-MAX, with approaches that are new to this area of applied econometrics, in particular, Artificial Neural Networks (ANN); Linear Regression Trees, Local Regressions and Generalised Additive Models. Section 2 presents the properties and definitions of the models to be compared and Section 3 the characteristics of the data used which in this case are spot electricity prices from the Californian market 07/1999-12/2000. This period includes the ’crisis’ months of May-August 2000 where extreme volatility was observed. Section 4 presents the results and ranking of methods on the basis of forecasting performance. Section 5 concludes.Electricty Time Series; Forecasting Performance; Semi- and Non- Parametric Methods

    Hybrid artificial intelligence algorithms for short-term load and price forecasting in competitive electric markets

    Get PDF
    The liberalization and deregulation of electric markets forced the various participants to accommodate several challenges, including: a considerable accumulation of new generation capacity from renewable sources (fundamentally wind energy), the unpredictability associated with these new forms of generation and new consumption patterns, contributing to further electricity prices volatility (e.g. the Iberian market). Given the competitive framework in which market participants operate, the existence of efficient computational forecasting techniques is a distinctive factor. Based on these forecasts a suitable bidding strategy and an effective generation systems operation planning is achieved, together with an improved installed transmission capacity exploitation, results in maximized profits, all this contributing to a better energy resources utilization. This dissertation presents a new hybrid method for load and electricity prices forecasting, for one day ahead time horizon. The optimization scheme presented in this method, combines the efforts from different techniques, notably artificial neural networks, several optimization algorithms and wavelet transform. The method’s validation was made using different real case studies. The subsequent comparison (accuracy wise) with published results, in reference journals, validated the proposed hybrid method suitability.O processo de liberalização e desregulação dos mercados de energia elétrica, obrigou os diversos participantes a acomodar uma série de desafios, entre os quais: a acumulação considerável de nova capacidade de geração proveniente de origem renovável (fundamentalmente energia eólica), a imprevisibilidade associada a estas novas formas de geração e novos padrões de consumo. Resultando num aumento da volatilidade associada aos preços de energia elétrica (como é exemplo o mercado ibérico). Dado o quadro competitivo em que os agentes de mercado operam, a existência de técnicas computacionais de previsão eficientes, constituí um fator diferenciador. É com base nestas previsões que se definem estratégias de licitação e se efetua um planeamento da operação eficaz dos sistemas de geração que, em conjunto com um melhor aproveitamento da capacidade de transmissão instalada, permite maximizar os lucros, realizando ao mesmo tempo um melhor aproveitamento dos recursos energéticos. Esta dissertação apresenta um novo método híbrido para a previsão da carga e dos preços da energia elétrica, para um horizonte temporal a 24 horas. O método baseia-se num esquema de otimização que reúne os esforços de diferentes técnicas, nomeadamente redes neuronais artificiais, diversos algoritmos de otimização e da transformada de wavelet. A validação do método foi feita em diferentes casos de estudo reais. A posterior comparação com resultados já publicados em revistas de referência, revelou um excelente desempenho do método hibrido proposto

    Energy Time Series Forecasting Based on Pattern Sequence Similarity

    Get PDF
    This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the prediction.Ministerio de Ciencia y Tecnología TIN2007- 68084-C-00Junta de Andalucia P07-TIC- 0261
    corecore