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The ongoing financial crisis has exacerbated concerns about the need to redefine the role and characteristics of regulation. In the case of public services, deregulation was justified by the benefits that would accrue to consumers, though scant attention was paid to actual consumer experience in regulatory design. New European public services regulation policies argue that the citizens' perspective should be considered, with the aim of improving the functioning of the markets whilst protecting the general interest. The decisions and attitudes of Spanish citizens regarding two public services, electricity and telecommunications, are analysed. The novelty of this article is to contrast revealed and declared preferences, a method which has not yet been used in analysing public service use. The results show when and how socio-economic variables affect service use and satisfaction and the conclusions indicate how the results could be used to formulate better policies to address issues of social inclusiveness. [ABSTRACT FROM AUTHOR]

Abstract: In new deregulated electricity market, price forecasts have become a fundamental input to an energy company’s decision making and strategy development process. However, the exclusive characteristics of electricity price such as non-stationarity, non-linearity and time-varying volatile structure present a number of challenges for this task. In spite of all performed research on this area in the recent years, there is still essential need for more accurate and robust price forecast methods. Besides, there is a lack of efficient feature selection technique for designing the input vector of electricity price forecast. In this paper, a new two-stage feature selection algorithm composed of modified relief and mutual information (MI) techniques is proposed for this purpose. Moreover, cascaded neural network (CNN) is presented as forecast engine for electricity price prediction. The CNN is composed of cascaded forecasters where each forecaster is a neural network (NN). The proposed feature selection algorithm selects the best set of candidate inputs which is used by the CNN. The proposed method is examined on PJM, Spanish and Ontario electricity markets and compared with some of the most recent price forecast techniques. [Copyright &y& Elsevier]