Deep Learning-Based Congestion Forecasting: A Literature Review and Future DOI
Mehdi Attioui, Mohamed Lahby

Опубликована: Окт. 26, 2023

The quick improvement of transportation systems gives rise to critical issues, the foremost vital which is traffic congestion, has numerous negative impacts such as long time travel and road rage. There are other long-term impacts. Forecasting congestion subsequently gotten be a key objective in optimising flow imporving quality life for people cities. Machine learning may awesome way predict flow, but Deep techniques have been shown more effective reducing congestion. reason paper conduct systematic mapping study examine categorise studies on deep strategies forecast Selected articles were categorized analyzed by channel year publication, type study, research context, vehicle applied To deal with this situation, majority papers use classification, prediction, regression techniques. It also found that most these algorithms deployed dataset speed flow. Many follow supervised learning, unsupervised or hybrid preferred data Convolutional Neural Networks Long Short-Term Memory.

Язык: Английский

Data-driven economic predictive control for sustainable management of renewable energy systems DOI Creative Commons

Makhbuba Shermatova,

Komila Ibragimova,

Dilyorjon Yuldashev

и другие.

E3S Web of Conferences, Год журнала: 2024, Номер 501, С. 01005 - 01005

Опубликована: Янв. 1, 2024

The transition to renewable energy sources is driven by the need reduce greenhouse gas emissions, mitigate climate change, and enhance security. Renewable sources, such as solar, wind, hydropower, are inherently intermittent, making their integration into power grid complex. This paper emphasizes significance of predictive modelling for optimization it establishes connection between machine learning economic model control techniques realization sustainable management sources. Machine Learning based frameworks can assist providers in preparing fluctuating supplies predicting demand forecasting production capabilities plants. Moreover, combining smart designs with proposed technique ensure consumer satisfaction while adhering sustainability requirements.

Язык: Английский

Процитировано

0
Assessment of Machine Learning Algorithms for Predicting Potential Solar and Wind Energy Locations DOI

Hicham Mhamdi,

Omar Kerrou,

Mourtadha Sarhan

и другие.

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 372 - 380

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Deep Learning-Based Congestion Forecasting: A Literature Review and Future DOI
Mehdi Attioui, Mohamed Lahby

Опубликована: Окт. 26, 2023

The quick improvement of transportation systems gives rise to critical issues, the foremost vital which is traffic congestion, has numerous negative impacts such as long time travel and road rage. There are other long-term impacts. Forecasting congestion subsequently gotten be a key objective in optimising flow imporving quality life for people cities. Machine learning may awesome way predict flow, but Deep techniques have been shown more effective reducing congestion. reason paper conduct systematic mapping study examine categorise studies on deep strategies forecast Selected articles were categorized analyzed by channel year publication, type study, research context, vehicle applied To deal with this situation, majority papers use classification, prediction, regression techniques. It also found that most these algorithms deployed dataset speed flow. Many follow supervised learning, unsupervised or hybrid preferred data Convolutional Neural Networks Long Short-Term Memory.

Язык: Английский

Процитировано

0