Blaz Bertalanic

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Verified

Blaz verified their affiliation via an institutional email.

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• PhD
• Researcher at Jožef Stefan Institute

Due to growing population and technological advances, global electricity consumption is increasing. Although CO2 emissions are projected to plateau or slightly decrease by 2025 due to the adoption of clean energy sources, they are still not decreasing enough to mitigate climate change. The residential sector makes up 25% of global electricity consu...

The freshness of information in an Internet of Things (IoT) system can be measured using the Age of Information (AoI). AoI is a process that evolves over time and displays a distinct saw-tooth pattern, providing system operators with real-time performance information of IoT devices. However, the plethora of proposed AoI-related metrics offers limit...

Time series classification is a relevant step supporting decision-making processes in various domains, and deep neural models have shown promising performance. Despite significant advancements in deep learning, the theoretical understanding of how and why complex architectures function remains limited, prompting the need for more interpretable mode...

With the process of democratization of the network edge, hardware and software for networks are becoming available to the public, overcoming the confines of traditional cloud providers and network operators. This trend, coupled with the increasing importance of AI in 6G and beyond cellular networks, presents opportunities for innovative AI applicat...

In this paper, we investigate the integration of Retrieval Augmented Generation (RAG) with large language models (LLMs) such as ChatGPT, Gemini, and Llama to enhance the accuracy and specificity of responses to complex questions about electricity datasets. Recognizing the limitations of LLMs in generating precise and contextually relevant answers d...

Due to growing population and technological advances, global electricity consumption, and consequently also CO2 emissions are increasing. The residential sector makes up 25% of global electricity consumption and has great potential to increase efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption da...

Energy management systems (EMS), as enablers of more efficient energy consumption, monitor and manage appliances to help residents be more energy efficient and thus more frugal. Recent appliance detection and identification techniques for such systems rely on machine learning. However, machine learning solutions for appliance classification on exis...

Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response applications and energy management systems as well as for awareness raising and motivation for improve...

Network softwarization, which shifts hardware-centric functions to software implementations, is essential for enhancing the agility of cellular and non-cellular wireless networks. This change, while raising reliability concerns, also improves system monitoring through digital twins. One example is the Digital Twin Edge Networks (DITEN), which enhan...

The number of end devices that use the last-mile wireless connectivity is dramatically increasing with the rise of smart infrastructures and requires reliable functioning to support smooth and efficient business processes. To efficiently manage such massive wireless networks, more advanced and accurate network monitoring and malfunction detection s...

In recent years, much work has been done on processing of wireless spectrum data involving machine learn- ing techniques in domain-related problems for cognitive radio networks, such as anomaly detection, modulation classification, technology classification and device fingerprinting. Most of the solutions are based on labeled data, created in a con...

In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning archi-tectures. In wireless networks, many studies were conducted in automatic learning of feature representations for domain-related challenges. However, most of the exist...

In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning architectures. In wireless networks, many studies were conducted in automatic learning of feature representations for domain-related challenges. However, most of the existi...

Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response applications and energy management systems as well as for awareness raising and motivation for improve...

Time series classification is an important task in many fields. In intrusive and non-intrusive load monitoring (N)ILM, time series data are obtained from power measurements of electrical appliances that are not known in advance, therefore extracting the type of appliance from the data is a relevant problem in smart grids. We propose a transformatio...

As the number of wireless end and edge devices increases, so does the volume of data to be monitored in view of predicting or detecting malfunctions. Furthermore, as the networks become more complex, the more context can be provided around a certain anomaly , fault or malfunction, the easier will be to establish mitigation actions in a fast and eff...

Driven by various academic, standardization and regulatory initiatives, recent research on spectrum resource utilisation has focused also on technology and transmission classification using various deep learning (DL) architectures. However, especially in unlicensed bands it is often hard to obtain labelled data of sufficient quality for training DL...

The so-called black-box deep learning (DL) models are increasingly used in classification tasks across many scientific disciplines, including wireless communications domain. In this trend, supervised DL models appear as most commonly proposed solutions to domain-related classification problems. Although they are proven to have unmatched performance...

The so-called black-box deep learning (DL) models are increasingly used in classification tasks across many scientific disciplines, including wireless communications domain. In this trend, supervised DL models appear as most commonly proposed solutions to domain-related classification problems. Although they are proven to have unmatched performance...

Artificial intelligence (AI) has emerged as a key technology in various domains, including telecommunications. The use of AI in cellular networks has the potential to revolutionize the way we communicate by enabling new capabilities, improving network efficiency, and providing better user experiences. However, domain specific data transformations,...

Location-based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular deep learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, wi...

Nowadays, modern man-made infrastructures are being upgraded with information and communication technologies that form large wireless networks. Such large wireless networks must be monitored to ensure reliable operation by the proactive detection and correction of link failures or abnormal network behaviour in view of uninterrupted business operati...

Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, wi...

In recent years, much work has been done on processing of wireless spectrum data involving machine learning techniques in domain-related problems for cognitive radio networks, such as anomaly detection, modulation classification, technology classification and device fingerprinting. Most of the solutions are based on labeled data, created in a contr...

Location based services, already popular with end users, are now inevitably becoming part of new wireless infras-tructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, w...

The digitization of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven pipelines is yet to be addressed. In this paper we perform a first time study on data models, energy feature engineering a...

Electrical management systems (EMS) are playing a central role in enabling energy savings. They can be deployed within an everyday household where they monitor and manage appliances and help residents be more energy efficient and subsequently also more economical. One of they key functionalities of EMS is to automatically detect and identify applia...

Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, wi...

Appliance load monitoring (ALM) is a technique that enables increasing the efficiency of domestic energy usage by obtaining appliance specific power consumption profiles. While machine learning have been shown to be suitable for ALM, the work on analyzing design trade-offs during the feature and model selection steps of the ML model development is...

Non-Intrusive load monitoring provides the users with detailed information about the electricity consumption of their appliances and gives energy providers a better insight about the usage of their clients. It can also be used in improving care of elderly, legal services and optimizing energy consumption. While there is plenty of work in NILM appli...

Machine learning (ML) techniques play a significant role in detecting anomalous wireless links. However, to date, to the extent of our knowledge, there is no robust classifier that would work in a realistic scenario where various anomalies could appear concurrently in the time-series gleaned from the network monitoring tools. In this paper, we prop...

The number of end devices that use the last mile wireless connectivity is dramatically increasing with the rise of smart infrastructures and require reliable functioning to support smooth and efficient business processes. To efficiently manage such massive wireless networks, more advanced and accurate network monitoring and malfunction detection so...