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The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architectures provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to ''anomalous'' load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen's Self Organizing Map (SOM) The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer perceptron with a backpropagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations

In-depth security analyses of power systems (PSs) require to consider the vulnerabilities to natural and human-related threats, which may cause multiple dependent contingencies. On the other hand, such events often lead to high impact on the system, so that decision-making aimed to enhance security may become difficult. Introducing the uncertainty, the risk associated to each contingency can be evaluated, thus allowing to perform effective contingency ranking. This paper describes an in-depth security assessment methodology, based on an ``extended'' definition of risk (including threats, vulnerability, contingency, and impact) aimed to perform the risk assessment of the integrated power and Information and Communication Technology (ICT) systems. The results of the application to test cases and realistic PSs show the added value of the proposed approach with respect to conventional security analyses in dealing with uncertainty of threats, vulnerabilities, and system response

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