[HTML][HTML] Digitalization in decarbonizing electricity systems–Phenomena, regional aspects, stakeholders, use cases, challenges and policy options
Digitalization is a megatrend that affects and transforms societal, economic, and
environmental processes on a global scale. Driven by a combination of technological …
environmental processes on a global scale. Driven by a combination of technological …
Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges
MF Elahe, M Jin, P Zeng - International Journal of Energy …, 2021 - Wiley Online Library
The collection and storage of large‐scale load data in a smart grid provide new approaches
for the efficient, economical, and safe operation of power systems. Deep Learning (DL) has …
for the efficient, economical, and safe operation of power systems. Deep Learning (DL) has …
A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions
Short-term building energy predictions serve as one of the fundamental tasks in building
operation management. While large numbers of studies have explored the value of various …
operation management. While large numbers of studies have explored the value of various …
Operating AI systems in the electricity sector under European's AI Act–Insights on compliance costs, profitability frontiers and extraterritorial effects
Artificial intelligence (AI) brings great potential but also risks to the electricity industry.
Following the EU's current regulatory proposal, the EU Regulation for Artificial Intelligence …
Following the EU's current regulatory proposal, the EU Regulation for Artificial Intelligence …
[HTML][HTML] Matching of everyday power supply and demand with dynamic pricing: Problem formalisation and conceptual analysis
The energy transition is expected to significantly increase the share of renewable energy
sources whose production is intermittent in the electricity mix. Apart from key benefits, this …
sources whose production is intermittent in the electricity mix. Apart from key benefits, this …
Data-driven inverse optimization for modeling intertemporally responsive loads
This letter proposes a novel framework for modeling the response-price relationship of
intertemporally responsive loads (IRL) using historical data. This task is cast as a data …
intertemporally responsive loads (IRL) using historical data. This task is cast as a data …
[HTML][HTML] Generating multivariate load states using a conditional variational autoencoder
For planning of power systems and for the calibration of operational tools, it is essential to
analyse system performance in a large range of representative scenarios. When the …
analyse system performance in a large range of representative scenarios. When the …
Unleashing the benefits of smart grids by overcoming the challenges associated with low-resolution data
Smart meters have been widely deployed worldwide, but there is an often-overlooked
problem that remains unresolved: the data collected from these meters is of relatively low …
problem that remains unresolved: the data collected from these meters is of relatively low …
Functional model of residential consumption elasticity under dynamic tariffs
K Ganesan, JT Saraiva, RJ Bessa - Energy and Buildings, 2022 - Elsevier
One of the major barriers for the retailers is to understand the consumption elasticity they
can expect from their contracted demand response (DR) clients. The current trend of DR …
can expect from their contracted demand response (DR) clients. The current trend of DR …
A variational autoencoder-based dimensionality reduction technique for generation forecasting in cyber-physical smart grids
Modern energy systems often regarded as smart grid (SG) systems are cyber-physical
systems (CPS) equipped with advanced metering and smart sensing devices, leading to a …
systems (CPS) equipped with advanced metering and smart sensing devices, leading to a …