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In this paper we address the problem of one day-ahead hourly electricity price forecast for smart grid applications. To this aim, we investigate the application of a number of predictive models for time-series, including methods based on empirical strategies frequently adopted in the smart grid community, Kalman Filters and Echo State Networks (ESNs). The considered methods have been suitably modified to address the electricity price forecast problem. Strategies based on daily re-adaptation of models’ parameters are taken into consideration as well. The predictive performance achieved by the considered models is assessed, and the methods are compared among each other on recent real data from the Italian electricity market. As a result of the comparison over three years data, ESN methods appear to provide the most accurate price predictions, which could imply significant economic savings in many smart grid activities, such as switching on power plants to support power generation from renewable sources, electric vehicle recharging or usage of household appliances. [ABSTRACT FROM AUTHOR]

Abstract: The paper discusses a model of robot perception based on a comparison loop process between the actual and the expected robot input sensory data generated by a 3D robot/environment simulator. The perception loop process is operating in CiceRobot, a functional robot architecture implemented on an autonomous robot RWI B21 offering guided tours at the Archaeological Museum of Agrigento, Italy. [Copyright &y& Elsevier]