Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
A comprehensive review on deep learning approaches for short-term load forecasting
Y Eren, İ Küçükdemiral - Renewable and Sustainable Energy Reviews, 2024 - Elsevier
The balance between supplied and demanded power is a crucial issue in the economic
dispatching of electricity energy. With the emergence of renewable sources and data-driven …
dispatching of electricity energy. With the emergence of renewable sources and data-driven …
Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach
In a modern power system with an increasing proportion of renewable energy, wind power
prediction is crucial to the arrangement of power grid dispatching plans due to the volatility …
prediction is crucial to the arrangement of power grid dispatching plans due to the volatility …
Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms
Building energy prediction has gained significant attention as a thriving research field owing
to its immense potential in enhancing energy efficiency within building energy management …
to its immense potential in enhancing energy efficiency within building energy management …
A novel machine learning-based electricity price forecasting model based on optimal model selection strategy
Current electricity price forecasting models rely on only simple hybridizations of data
preprocessing and optimization methods while ignoring the significance of adaptive data …
preprocessing and optimization methods while ignoring the significance of adaptive data …
Deterministic policy gradient algorithms
In this paper we consider deterministic policy gradient algorithms for reinforcement learning
with continuous actions. The deterministic policy gradient has a particularly appealing form …
with continuous actions. The deterministic policy gradient has a particularly appealing form …
Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting
Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also
has a great interest to investors and energy policy maker as well as government. Literature …
has a great interest to investors and energy policy maker as well as government. Literature …
Short-term electrical load forecasting using hybrid model of manta ray foraging optimization and support vector regression
S Li, X Kong, L Yue, C Liu, MA Khan, Z Yang… - Journal of Cleaner …, 2023 - Elsevier
Demand prediction is playing a progressively important role in electricity management, and
is fundamental to the corresponding decision-making. Because of the high variability of the …
is fundamental to the corresponding decision-making. Because of the high variability of the …
Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization
J Shi, C Li, X Yan - Energy, 2023 - Elsevier
State-of-art artificial intelligence (AI) has made great breakthroughs in various industries.
Ensemble learning mixed with various predictors provides a considerable solution for …
Ensemble learning mixed with various predictors provides a considerable solution for …
[HTML][HTML] A taxonomy of machine learning applications for virtual power plants and home/building energy management systems
A Virtual power plant is defined as an information and communications technology system
with the following primary functionalities: enhancing renewable power generation …
with the following primary functionalities: enhancing renewable power generation …