Event cameras are novel vision sensors that output pixel-level brightness changes ("
events") instead of traditional video frames. These asynchronous sensors offer several …

Virtual power plants (VPPs) possess the capability to aggregate flexible resources to provide
grid services in the distributed network operation. The potential for flexibility utilization in …

In recent years, transformer-based models have shown state-of-the-art results for Natural
Language Processing (NLP). In particular, the introduction of the BERT language model …

Accurate load prediction is an effective way to reduce power system operation costs.
Traditionally, the Mean Square Error (MSE) is a common-used loss function to guide the …

The identification of the multiple model-based Hammerstein parameter varying systems is
studied in this paper. The parameters of the considered systems vary as the systems perform …

The loss surfaces of deep neural networks have been the subject of several studies,
theoretical and experimental, over the last few years. One strand of work considers the …

Physics-informed neural networks (PINNs) and their variants have recently emerged as
alternatives to traditional partial differential equation (PDE) solvers, but little literature has …

Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of
accuracy and speed in learning algorithms. This paper offers progress on both fronts. It …

Load forecasting is of great significance in the power industry as it can provide a reference
for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits …

Nowadays, the changing paradigm in power systems highlights the necessity of improving
detail in energy models. The deployment of non-dispatchable renewable energy resources …