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Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study
This paper provides an extensive review of learning-based short-term forecasting models for
smart grid applications. In addition to this, the paper also explores forecasting models …
smart grid applications. In addition to this, the paper also explores forecasting models …
Application of artificial intelligence in solar and wind energy resources: a strategy to deal with environmental pollution
Environmental pollution has become a significant concern of nations. International
organizations, local authorities, and social activists try to achieve sustainable development …
organizations, local authorities, and social activists try to achieve sustainable development …
Learning approach for energy consumption forecasting in residential microgrid
Residential energy consumption plays an important role in the social and economic
development of the country. Highly accurate forecasting can aid in decision making and …
development of the country. Highly accurate forecasting can aid in decision making and …
A comparative analysis of supervised machine learning algorithms for electricity demand forecasting
Accurate electricity demand forecasting is crucial for efficient, economical and stable
operation of power system grid. With the increasing integration of intermittent sources, need …
operation of power system grid. With the increasing integration of intermittent sources, need …
Wind power data cleaning using RANSAC-based polynomial and linear regression with adaptive threshold
H Yang, J Tang, W Shao, J Yin, B Liu - Scientific Reports, 2025 - nature.com
As the global demand for clean energy continues to rise, wind power has become one of the
most important renewable energy sources. However, wind power data often contains a high …
most important renewable energy sources. However, wind power data often contains a high …
Sales forecasting in the electrical industry-an illustrative comparison of time series and machine learning approaches
D Büttner, M Rabe - … 9th International Conference on Traffic and …, 2021 - ieeexplore.ieee.org
Sales forecasts are required for planning resources and defining stock levels through the
supply chain (SC) because demand is becoming diversified due to higher customer …
supply chain (SC) because demand is becoming diversified due to higher customer …
Multi-agent based cloud energy storage framework for residential community
Energy storage is substantially admitted as an immense potential for distributed energy
sources in the smart grid and load balancing. It is an enabling aid to the adaptation of …
sources in the smart grid and load balancing. It is an enabling aid to the adaptation of …
[PDF][PDF] Automated adaptive-ensemble framework for large wind power prediction in poland using deep learning models
Prediction of considerable wind power is a significant factor in modern power systems'
robust and resilient operation. As a result, many studies addressed up-to-day-ahead wind …
robust and resilient operation. As a result, many studies addressed up-to-day-ahead wind …
Attention Mechanism in Deep Learning for Wind Power Forecasting
Wind energy is the fast moving renewable energy sources in the world. However, wind
energy deals with many challenges namely seed capital, motionless property of wind plants …
energy deals with many challenges namely seed capital, motionless property of wind plants …
P2P Energy Trading with Decentralized Energy Storage Embedded Network Loss
The deployment of distributed energy sources (DES), has transformed ordinary consumers
into active par-ticipants in the local energy market (LEM). Digitalization of distribution system …
into active par-ticipants in the local energy market (LEM). Digitalization of distribution system …