Day-Ahead Price Forecasting for the Spanish Electricity Market

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

A Comparison of Two Techniques for Next- Day Electricity Price Forecasting

In the framework of competitive markets, the market’s participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported

A Comparison of Two Techniques for Next-Day Electricity Price Forecasting, ser

Abstract. In the framework of competitive markets, the market's participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported

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

[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. 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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). 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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

Framework for collaborative intelligence in forecasting day-ahead electricity price

Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts

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

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

Forecasting of electricity prices in the Spanish electricity market using machine learning tools

The objective of this research assignment was to forecast electricity prices in the Spanish electricity market using three different machine learning techniques: k-nearest neighbours, support vector regression and artificial neural networks. The achieved results were compared and the quality of developed models was evaluated. The project was implemented in Python3.Incomin

Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting

Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting. In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices

Combining hydro-generation and wind energy biddings and operation on electricity spot markets

Wind generation is growing rapidly in all the world, especially in Europe. The power produced by this kind of generation is difficult to predict and the predictions are not very accurate. In most systems these imbalances are costly. These penalties reduce the revenue for the wind generation company (WGENCOs). An option to solve this problem would be to work together with another agent. In this paper, a combined strategy for bidding and operating in a power exchange is presented. It considers the combination of a WGENCO and a hydro-generation company (HGENCO). The mathematical formulation for the optimal bids and for the optimal operation is presented, as well as results from realistic cases.Publicad