An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine
This paper proposes a novel and hybrid intelligent algorithms to directly modelling
prediction intervals (PIs), as an accurate, optimum, reliable and high efficient wind power …
prediction intervals (PIs), as an accurate, optimum, reliable and high efficient wind power …
Adaptive inference for dynamic passenger route usage patterns in a metro network considering time-varying and heavy-tailed travel times
Z Shi, W Shen, P Schonfeld, Y Liu, N Zhang - Transportation Research Part …, 2025 - Elsevier
Due to the dynamic changes in timetables, passenger demand, and passenger composition,
the distribution of passengers within a metro system becomes quite complex. Many studies …
the distribution of passengers within a metro system becomes quite complex. Many studies …
Comparative assessment of regression techniques for wind power forecasting
Considering the escalating rates of exhaustion of non-renewable energy resources, coupled
with the harmful environmental side effects of harnessing them (eg damage to public health …
with the harmful environmental side effects of harnessing them (eg damage to public health …
Determine Q–V characteristics of grid-connected wind farms for voltage control using a data-driven analytics approach
Due to the varying and intermittent nature of wind resource, grid-connected wind farms pose
significant technical challenges to power grid on power quality and voltage stability. Wind …
significant technical challenges to power grid on power quality and voltage stability. Wind …
Traffic outlier detection by density-based bounded local outlier factors
J Tang, HYT Ngan - Information Technology in Industry, 2016 - it-in-industry.org
Outlier detection (OD) is widely used in many fields, such as finance, information and
medicine, in cleaning up datasets and kee** the useful information. In a traffic system, it …
medicine, in cleaning up datasets and kee** the useful information. In a traffic system, it …
Short term wind power forecasting using k-nearest neighbour (KNN)
R Mahaseth, N Kumar, A Aggarwal… - Journal of Information …, 2022 - Taylor & Francis
This project focuses on prediction of energy power based on data of previous 2 years using
various machine learning algorithms. The data is analysed on yearly basis. The wind power …
various machine learning algorithms. The data is analysed on yearly basis. The wind power …
Wind ramp event prediction with parallelized gradient boosted regression trees
S Gupta, NA Shrivastava, A Khosravi… - … Joint Conference on …, 2016 - ieeexplore.ieee.org
Accurate prediction of wind ramp events is critical for ensuring the reliability and stability of
the power systems with high penetration of wind energy. This paper proposes a …
the power systems with high penetration of wind energy. This paper proposes a …