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Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
A hybrid framework for forecasting power generation of multiple renewable energy sources
The accurate power generation forecast of multiple renewable energy sources is significant
for the power scheduling of renewable energy systems. However, previous studies focused …
for the power scheduling of renewable energy systems. However, previous studies focused …
[HTML][HTML] Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction
The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent
nature of the solar resource highlights the importance of power forecasting for the grid …
nature of the solar resource highlights the importance of power forecasting for the grid …
Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …
Nevertheless, the conventional" trial and error" method for producing advanced …
Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations
Accurate regional distributed PV power forecasting provides data support for power grid
management and optimal operation. Distributed PV has the characteristics of large quantity …
management and optimal operation. Distributed PV has the characteristics of large quantity …
[HTML][HTML] A state-of-art-review on machine-learning based methods for PV
In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with
applications in several applicative fields effectively changing our daily life. In this scenario …
applications in several applicative fields effectively changing our daily life. In this scenario …
Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis
The rapid development of the photovoltaic industry provides a new source of power for the
continued operation of the over-consumed energy world. While providing new opportunities …
continued operation of the over-consumed energy world. While providing new opportunities …
A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time war** distance and deep autoencoder
M Yang, M Zhao, D Huang, X Su - Renewable Energy, 2022 - Elsevier
The improvement of photovoltaic (PV) power prediction precision plays a crucial role in the
new energy consumption. This paper proposes a composite prediction framework (DC (DWT …
new energy consumption. This paper proposes a composite prediction framework (DC (DWT …
A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants
Recently, clean solar energy has aroused wide attention due to its excellent potential for
electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) …
electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) …
A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches
This review reports a quantitative analysis across the deterministic photovoltaic (PV) power
forecasting approaches. Model accuracy tests from papers passing a set of selection criteria …
forecasting approaches. Model accuracy tests from papers passing a set of selection criteria …