Electricity price forecasting accounting for renewable energies: optimal combined forecasts

Abstract

Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al (2007 and2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24 h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models (according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combination of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to December 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.

Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests

“…What is really new in this paper is to consider the energy produced by the different technologies and to assess their effect on prices. Some studies [23,24] just include renewable energy production, particularly wind power. In this article thermal generation is introduced in the data base through different variables which account for the energy introduced in the market from nuclear power plants (V7), coal plants (V8), fuel plus gas (V9) and combined cycle (V10).…”

“…Several studies [23,36,38] investigate on the optimum length of the training set, for instance [38] suggests 50 days. It is noteworthy that at present we do not want to discuss the optimal length of the training sample, not by the moment, because the main goal of this work is to explore and highlight the predictive ability of these techniques and to hold over the most accurate prediction study for future works.…”

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

“…In this paper, we focus on short-term (one-day-ahead) electricity price forecasting and we analyze the correlation between the renewable energy production and electricity price through historical records. The work in [6] uses the amount of wind energy and hydro energy, the most relevant renewable energy sources in the Iberian Market, to come up with an optimal model for one-day-ahead electricity price forecasting. In the Italian electricity market, which is currently facing a low-carbon transition, electricity prices are highly influenced by the generation from renewable sources [7].…”

Electricity Price Forecasting by Averaging Dynamic Factor Models

“…Furthermore, [20] obtain forecasts for the daily average price employing dimensionality reduction techniques, as well as the forecast combination of several models for hourly prices. Other references are [21], who use averaging to obtain wind speed, solar irradiation and temperature forecasts, which are employed to estimate prices; and [22], who forecast hourly electricity prices for the Spanish market by weighting seasonal ARIMA (with exogenous variables) and seasonal dynamic factor models of similar performance.…”