The increasing digitalization of energy infrastructures, particularly electric vehicle (EV) charging systems, has expanded their vulnerability to cyber threats. This paper presents a modular AI-driven platform for detecting attacks on EV charging infrastructures. The platform combines real-time data collection tools (Tshark, Nozomi Guardian, SNMP/Syslog, Elasticsearch Logstash and Kibana (ELK)) with Long Short-Term Memory (LSTM) Autoencoder-based anomaly detection. Data were gathered from a charging facility at RSE with twelve OCPP-J v1.6 charging stations, including normal and simulated Denial of Service (DoS) scenarios. The model, trained on multivariate time-series traffic data, achieved 97.1% Accuracy and 98.6% Recall, ensuring robust anomaly detection and minimizing false negatives. Although Precision was lower (52%) due to traffic variability, the system effectively detected both cyber-induced and operational anomalies, such as station disconnections. This work demonstrates the value of integrating deep learning with real-time monitoring to enhance the resilience of smart energy systems. Future developments will focus on improving precision, expanding protocol coverage, and addressing advanced threats such as data injection and Man-in-the-Middle attacks.

The growth of photovoltaic (PV) installations is essential for the global energy transition; however, comprehensive data regarding their spatial distribution are limited, which complicates effective energy planning. This research introduces a methodology for automatic recognition of ground-mounted PV systems in Italy, using semantic segmentation and Sentinel-2 RGB images with a resolution of 10 m. The objective of this methodology is to accurately identify both the locations and the sizes of these installations, estimate their capacity, and facilitate regular updates to maps, thereby supporting energy planning strategies. The segmentation model, which is founded on a U-Net architecture, is trained using a dataset from 2019 and evaluated on two separate cases that involve different dates and geographical areas. We propose a multi-temporal approach, applying the model to a sequence of images taken throughout the year and aggregating the results to create a PV detection probability map. Users have the flexibility to modify probability thresholds to enhance accuracy: lower thresholds increase producer accuracy, ensuring continuous area detection for capacity estimation, while higher thresholds boost user accuracy by reducing false positives. Additionally, post-processing techniques, such as filtering for plastic-covered greenhouses, assist minimizing detection errors. However, there is a need for improved model generalizability across various landscapes, necessitating retraining with images from a range of environmental contexts.

A very serious problem affecting high voltage lines and stations is the phenomenon of surface discharges on insulators, with consequent possible service interruptions. In this paper, it is presented a novel approach in the detection of surface discharges, based on a deep learning and computer vision framework: the Video Discharge Extractor. The research has been carried out in the frame of an active collaboration with TERNA (the Italian TSO). The framework is able to process automatically the videos collected by the camera of the LANPRIS insulators ageing and monitoring station. The artificial intelligent system, based on an object detection approach using a convolutional neural network, has been improved and incorporated into an image processing chain able to maximise the information extracted from the monitoring videos. Such a framework could be a valid instrument for the TSO to process the videos to monitor the state of the ageing of the insulators and to detect any surface discharge phenomena. The framework developed is described in details and it is shown the potential of such an approach to image analysis in the context of the LANPRIS experimentation and not only. This work can act as a paradigm for further applications in the electro-energetic system where there is a need to analyse images and videos.