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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. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the development and evaluation of neural network based identification algorithms for the prediction of outdoor air temperature using acquired data from four European cities (Ancona – Italy, Chania – Greece, Granada – Spain and Mollet – Spain). Different neural network topologies (feed forward, cascade and elman) have been tested to identify the most suitable for each city. The efficiency of the prediction is validated by comparing predicted and measured outdoor air temperature. Furthermore, statistical tools such as R 2 , and root mean square error (rmse) are used to evaluate the annual performance of the neural network. The comparison of measured and predicted outdoor air temperature ( R 2 > 0.9, rmse <2 °C) confirms the accurate training of the neural network for all four European cities. All work has been contacted using Matlab's environment. [ABSTRACT FROM AUTHOR]