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

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

Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.Erratum: https://doi.org/10.1016/j.apenergy.2018.06.131Team DeSchutte

Flexibility quantification in the context of flexible heat and power for buildings

The European Horizon 2020 project Flexible Heat and Power (H2020 FHP) investigates how to exploit the thermal inertia of buildings and their Heating Ventilation and Air-Conditioning (HVAC) systems as a source of flexibility to offer Demand Response (DR) strategies. The goal of this project is to provide a framework that allows increasing the energy state of buildings during generation peaks and lowering their energy use when supply is scarce (and thus expensive), while respecting the indoor thermal comfort. In order to achieve that goal, a Dynamic Coalition Manager (DCM) architecture has been defined. To estimate the cost of changing the demand behaviour a measure for flexibility is required as well, i.e., the capacity of the load to behave differently compared to the baseline scenario. This flexibility quantification is needed to estimate the flexibility offer that the DCM can make to other market players such as the Distribution System Operators (DSOs) and Balance Responsible Parties (BRPs). Hence, the chosen flexibility indicator must be scalable since it has to be aggregated for a cluster of buildings. There already exist several ways of quantifying thermal flexibility of buildings. However, assessing and comparing the different definitions is a complicated task since the suitability of each indicator for its specific application is crucial. In this paper a flexibility quantification based on multiple Model Predictive Control (MPC) strategies is developed for an individual building, which is aggregated for a cluster of buildings. The flexibility indicator is demonstrated using grey-box models for the BAs.status: Published onlin

An accurate model for the determination of the kinetic coefficients of the copper-catalyzed oxidation of iodide by oxygen in an aqueous acidic medium

The reaction rate of the copper-catalyzed oxidation of iodide by oxygen in an aqueous acidic medium is first order in copper and oxygen concentrations, Michaelis-Menten in pH and a complex, asymmetrical bell shaped function in iodide concentrations. A theoretical, multivariate reaction rate equation was proposed which enabled to optimise the various kinetic coefficients. During the parameter optimisation, the experiments were weighted taking into account all measurement uncertainties, i.e. both on the reaction rate (output) and on the reactants' concentrations (inputs). This has two important advantages: (1) the model can be statistically checked to be acceptable for the description of the available measurements, and (2) the parameter uncertainties can be estimated accurately. In this case study. the model was indeed validated to be acceptable and the kinetic parameters could be estimated with a standard deviation between 5% (minimum) and 16% (maximum). As an application of this model, copper concentrations can be determined. (C) 2009 Elsevier B.V. All rights reserved

A generalized model for short-term forecasting of solar irradiance

In recent years, as the share of solar power in the electrical grid has been increasing, accurate methods for forecasting solar irradiance have become necessary to manage the electrical grid. More specifically, as solar generators are geographically dispersed, it is very important to have general models that can predict solar irradiance without the need of ground data. In this paper, we propose a novel technique that can accomplish that: using satellite images, the proposed model is able to forecast solar irradiance without the need of ground measurements. To illustrate the performance of the proposed model, we consider 15 locations in The Netherlands, and we show that the proposed model is as accurate as local models that are individually trained with ground data

Diebold-Mariano test results for paper "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms"

This dataset comprise the results of the Diebold-Mariano (DM) tests when comparing several models for predicting day-ahead electricity prices in Belgium in the years 2015-2016. These results are part of the research paper: Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms The results are provided for each of the 27 models evaluated in the original paper. In particular, there are 27 files representing the p-values of the statistical tests, each one of the corresponding to one model. The name of the file represents the model evaluated. The results of the DM tests are given defining the model under evaluation as M1 (as defined in the original research paper), and then considering the remaining 26 models as M2. The DM test considers the null hypothesis of the forecasts of M1 being equal or worse than those of M2 against the alternative hypothesis of the forecasts of M1 being better. The p-values represent the probability of observing the obtained experimental data if the null hypothesis is true. Thus, very low p-values represent the cases where the model evaluated (M1) is significantly better than its counterparts. For each model pair M1, M2, the results are also given for the 24 predictive horizons as well as for the full loss differential with serial correlation (as defined in the original paper). In addition, figures representing the DM results are also included. Instead of plotting the p-values, the test statistics are employed; this is done to obtain figures that are easier to read. A threshold line is also depicted to indicate the test statistic value that represents a p-value of 0.05

Estimation of heteroscedastic measurement noise variances

For any quantitative data interpretation it is crucial to have information about the noise variances. Unfortunately, this information is often unavailable a priori. We propose a procedure to estimate the noise variances starting from the residuals. The method takes two difficulties into account. (i) The noise can be heteroscedastic (not constant over the measurement domain). This implies that one number is not enough anymore to characterise the total noise variance structure. (ii) The initial model used to generate the residuals may be imperfect. As a consequence, the residuals contain more than only stochastic information. The outcome of our procedure is an estimate of the noise variances which depends on the sample number, but is independent of the postulated model. A by-product of the procedure is information about the distribution of the degrees of freedom over the measurement domain. Indeed, as a consequence of the heteroscedastic noise, the model parameters will be fitted more to those data with low uncertainty and most of the degrees of freedom are lost at these locations

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

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.Accepted Author ManuscriptTeam DeSchutte

A generalized model for short-term forecasting of solar irradiance

In recent years, as the share of solar power in the electrical grid has been increasing, accurate methods for forecasting solar irradiance have become necessary to manage the electrical grid. More specifically, as solar generators are geographically dispersed, it is very important to have general models that can predict solar irradiance without the need of ground data. In this paper, we propose a novel technique that can accomplish that: using satellite images, the proposed model is able to forecast solar irradiance without the need of ground measurements. To illustrate the performance of the proposed model, we consider 15 locations in The Netherlands, and we show that the proposed model is as accurate as local models that are individually trained with ground data.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team DeSchutterDelft Center for Systems and Contro