Incentive-based demand response for smart grid with reinforcement learning and deep neural network
R Lu, SH Hong - Applied energy, 2019 - Elsevier
Balancing electricity generation and consumption is essential for smoothing the power grids.
Any mismatch between energy supply and demand would increase costs to both the service …
Any mismatch between energy supply and demand would increase costs to both the service …
Prediction-based multi-agent reinforcement learning in inherently non-stationary environments
Multi-agent reinforcement learning (MARL) is a widely researched technique for
decentralised control in complex large-scale autonomous systems. Such systems often …
decentralised control in complex large-scale autonomous systems. Such systems often …
Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for …
Electrical load and wind power forecasting are a demanding task for modern electrical
power systems because both are closely linked with the weather parameters, such as …
power systems because both are closely linked with the weather parameters, such as …
Baseline methodologies for small scale residential demand response
Demand response (DR) programs are designed to reduce electricity load in periods of peak
electricity demand, which helps avoiding expensive network upgrades. This reduction is …
electricity demand, which helps avoiding expensive network upgrades. This reduction is …
Accurate and efficient selection of the best consumption prediction method in smart grids
Smart grids are becoming popular with the advent of sophisticated smart meters. They allow
utilities to optimize energy consumption during peak hours by applying various demand …
utilities to optimize energy consumption during peak hours by applying various demand …
A deep learning method for short-term dynamic positioning load forecasting in maritime microgrids
Featured Application Application of deep learning techniques to dynamic positioning in
maritime microgrids for power management system. Abstract The dynamic positioning (DP) …
maritime microgrids for power management system. Abstract The dynamic positioning (DP) …
A machine learning approach to modelling escalator demand response
This article relates to the topic of the escalator demand response potential. Previous studies
mapped escalators as an unrealized potential for additional demand response. The …
mapped escalators as an unrealized potential for additional demand response. The …
Generating electrical demand time series applying SRA technique to complement NAR and sARIMA models
JL Tena García, E Cadenas Calderón, E Rangel Heras… - Energy Efficiency, 2019 - Springer
Prediction of demand time series is commonly approached to deliver a punctual forecast
model, however, is highly recommended to provide probabilistic models that give a range to …
model, however, is highly recommended to provide probabilistic models that give a range to …
Decentralised multi-agent reinforcement learning for dynamic and uncertain environments
Multi-Agent Reinforcement Learning (MARL) is a widely used technique for optimization in
decentralised control problems. However, most applications of MARL are in static …
decentralised control problems. However, most applications of MARL are in static …
Flexibility forecast and resource composition methodology for virtual power plants
C Iraklis, J Smend, A Almarzooqi… - … Computer and Energy …, 2021 - ieeexplore.ieee.org
As more distributed generation, of different sizes, is being integrated into the electricity
network, energy systems become more decentralized and therefore need to adapt to the …
network, energy systems become more decentralized and therefore need to adapt to the …