Ricerc@Sapienza

In this paper, the authors discuss the crucial aspects of the insulation resistance testing of PV installations. This test verifies the integrity of wiring systems, and can be used to detect or prevent damages to wiring and ground faults. The insulation resistance test measures the resistance between ungrounded circuits and ground, under the application of high-voltage. Baseline insulation resistance can be compared to measurements over time to assess degradation of PV arrays or conductors.

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels.

Renewable electricity generation has variable and non-dispatchable output that rises several technical, economic and feasibility concerns, calling for energy storage capacity and forecasting techniques to allow the integration of large amounts of variable generation into existing grids. These problems need careful attention in small islands that are not connected to the national transmission grid. In this paper, we present a study for the small Italian island of Ponza on the use of Echo State Networks to forecast real-world energy time series.

In this paper, a new approach on energy time series prediction is carried out. We propose a deep learning technique with the employment of specific neural network architectures: Convolutional Neural Network and Long Short-Term Memory network. The goal is to exploit the correlation between several time series, joining and filtering them together as to bring out the long-term dependencies among all the observations. We superpose many different functional layers, thus providing a stacked scheme that can result in a greater approximation capability.