The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

Predictive Maintenance, Prognostics and Reliability Centered Maintenance approach are becoming more and more important in the railway sector to reduce costs of operation and increase reliability and safety. In fact they are fundamental to optimize the maintenance process, defining new measures and algorithms to locate faults, monitor health conditions of subsystems and estimate residual life of components. However there is a tradeoff to identify: new measures and new algorithms imply new sensors and new processing devices, and these items have their own cost and their own reliability; this issue has to be taken into account to evaluate the global benefit. In some cases, however, it's possible to use existing sensors and existing processing hardware to extract new information from already available data. It's clear that this is usually the best option because the benefit can be achieved with little or not cost at all. This paper describes the result of a study performed with the aim of detecting arcing events without the need of additional equipment mounted on board the train. A set of data relative to voltage and current collected by trains in high-speed lines together with a set of measurements coming from photosensors are available. The data are processed by the use of an advanced classification technique, namely Support Vector Machines, with the aim of extracting important information such as the time coordinate related to anomalies in the overhead contact line and the status of the contact strip of the pantograph.

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In this paper we present a novel method for daily short-term load forecasting, belonging to the class of “similar shape” algorithms. In the proposed method, a number of parameters are optimally tuned via a multi-objective strategy that minimizes the error and the variance of the error, with the objective of providing a final forecast that is at the same time accurate and reliable. We extensively compare our algorithm with other state-of-the-art methods. In particular, we apply our approach upon publicly available data and show that the same algorithm accurately forecasts the load of countries characterized by different size, different weather conditions, and generally different electrical load profiles, in an unsupervised manner