In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore,...
This abstract describes the smart grid management system is an emerging technology that utilizes mac...
Recent advances in computing technologies and the availability of large amounts of heterogeneous dat...
This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs...
In recent years, digitalisation has rendered machine learning a key tool for improving processes in ...
Machine learning (ML) applications have seen tremendous adoption in power system research and applic...
Machine learning (ML) models have been widely used in diverse applications of energy systems such as...
This volume deals with recent advances in and applications of computational intelligence and advance...
PhD thesis in Information technologyDigitalization and decentralization of energy supply have introd...
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy ...
The electric power system infrastructure is essential for modern economies and societies, as it prov...
In an increasingly applied domain of pervasive computing, sensing devices are being deployed progres...
With population increases and a vital need for energy, energy systems play an important and decisive...
peer reviewedThis paper reviews recent works applying machine learning techniques in the context of ...
This study utilizes machine learning and, more specifically, reinforcement learning (RL) to allow fo...
This abstract describes the smart grid management system is an emerging technology that utilizes mac...
Recent advances in computing technologies and the availability of large amounts of heterogeneous dat...
This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs...
In recent years, digitalisation has rendered machine learning a key tool for improving processes in ...
Machine learning (ML) applications have seen tremendous adoption in power system research and applic...
Machine learning (ML) models have been widely used in diverse applications of energy systems such as...
This volume deals with recent advances in and applications of computational intelligence and advance...
PhD thesis in Information technologyDigitalization and decentralization of energy supply have introd...
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy ...
The electric power system infrastructure is essential for modern economies and societies, as it prov...
In an increasingly applied domain of pervasive computing, sensing devices are being deployed progres...
With population increases and a vital need for energy, energy systems play an important and decisive...
peer reviewedThis paper reviews recent works applying machine learning techniques in the context of ...
This study utilizes machine learning and, more specifically, reinforcement learning (RL) to allow fo...
This abstract describes the smart grid management system is an emerging technology that utilizes mac...
Recent advances in computing technologies and the availability of large amounts of heterogeneous dat...
This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs...