Machine learning methods for short‐term bid forecasting in the renewable energy market: A case study in Italy

Abstract

In liberalized markets, there usually exists a day-ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day.In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day-ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1-day-ahead forecast.The input-output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012-2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play. KEYWORDS day-ahead forecasting, extreme learning machines (ELMs), feature selection, machine learning, support vector regression (SVR), wind power market INTRODUCTIONOver the last few years, there has been an increasing interest in the study of numerical predictive models related to renewable energies, which are a serious alternative to replace traditional energy sources. Renewable energies represent a suitable and economical alternative to conventional power plants, but they are characterized by a high degree of variability. To deal with these uncertainties, several machine learning and feature selection techniques have been adopted for various forecasting tasks related to renewable energy.One of the most important and widely studied problems is the forecasting of energy production of power plants. Machine learning techniques have been widely used to obtain a mapping from weather data, generally wind speed, to the power generated by the turbines of a power plant. In the work of Heinermann and Kramer, 1 2 machine learning models, namely, regression trees and support vector regression (SVR), are combined with an ensemble method. Support vector regression is used also in the work of Zeng and Qiao, 2 where a new kernel, based on the wavelet mother function, is shown to outperform the more commonly used radial basis function kernel. In the work of Wan et al, 3 extreme learning machines (ELMs) are used as a fast and reliable alternative to the more traditional ...

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