The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …

Microgrid (MG) technologies offer users attractive characteristics such as enhanced power
quality, stability, sustainability, and environmentally friendly energy through a control and …

Smart energy has evolved over the years to include multiple domains integrated across
multiple technology themes, such as electricity, smart grid, and logistics, linked through …

The large-scale roll-out of smart meters allows the collection of a vast amount of fine-grained
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …

Smart meter data is a cornerstone for the realization of next-generation electrical power
grids by enabling the creation of novel energy data-based services like providing …

The increased penetration of Renewable Energy Sources (RES) as part of a decentralized
and distributed power system makes net-load forecasting a critical component in the …

The key advantage of using power-utility-owned smart meters is the ability to transmit
electrical energy consumption data to power utilities' remote data centers for various …

Safeguarding the data collected by smart meters is essential because the disclosure of this
information may threaten the privacy of the consumer. By obtaining them, hackers can find …

Load forecasting is one of the main concerns for power utility companies. It plays a
significant role in planning decisions, scheduling, operations, pricing, customer satisfaction …

This paper develops a novel hybrid model for forecasting electrical consumption based on
several deep learning and optimization models such as Support Vector Regression (SVR) …