As the energy system transitions to an intelligent smart grid with a mostly renewable energy
supply, synthetic energy time series are required to facilitate the development and …

Forecasting models that are trained across sets of many time series, known as Global
Forecasting Models (GFM), have shown recently promising results in forecasting …

The availability of fine grained time series data is a pre-requisite for research in smart-grids.
While data for transmission systems is relatively easily obtainable, issues related to data …

Time series data is widely used in scenarios such as supply chain, stock data analysis, and
smart manufacturing. A number of time series database systems have been invented to …

Forecasting energy demand of residential buildings plays an important role in the operation
of smart cities, as it forms the basis for decision-making in the planning and operation of …

A key performance indicator (KPI) consists of critical time series data that reflect the runtime
states of network systems (eg, response time and available bandwidth). Despite the …

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past
few years. Their increased penetration introduces both challenges and opportunities; they …

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis,
impeding the development and broad adaptation of accurate and reliable deep-learning …

For more than three decades, researchers have been developping generation methods for
the weather, energy, and economic domain. These methods provide generated datasets for …

With the rapid development of smart grids and increasing data collected in these networks,
analyzing this massive data for applications such as marketing, cyber-security, and …