Artificial intelligence for forecasting the prevalence of COVID-19 pandemic: An overview
Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting
publications has been recorded. Both statistical and artificial intelligence (AI) approaches …
publications has been recorded. Both statistical and artificial intelligence (AI) approaches …
Bayesian CNN-BiLSTM and vine-GMCM based probabilistic forecasting of hour-ahead wind farm power outputs
The importance of the accurate forecasting of power outputsof wind-based generation
systems is increasing, as their contributions to the total system generation are rising …
systems is increasing, as their contributions to the total system generation are rising …
[HTML][HTML] Separable shadow Hamiltonian hybrid Monte Carlo for Bayesian neural network inference in wind speed forecasting
Accurate wind speed and consequently wind power forecasts form a critical enabling tool for
large scale wind energy adoption. Probabilistic machine learning models such as Bayesian …
large scale wind energy adoption. Probabilistic machine learning models such as Bayesian …
Stock price prediction using sentiment analysis
We investigate the influence of financial news headline sentiment on the predictability of
stock prices using Long Term Short Term Memory (LSTM) networks. The investigation is …
stock prices using Long Term Short Term Memory (LSTM) networks. The investigation is …
Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data
Neural networks are increasingly used to predict wind direction and speed, two important
factors for estimating a wind farm's potential power output. However classical neural …
factors for estimating a wind farm's potential power output. However classical neural …
Short-term solar power forecasting using genetic algorithms: An application using south african data
Renewable energy forecasts are critical to renewable energy grids and backup plans,
operational plans, and short-term power purchases. This paper focused on short-term …
operational plans, and short-term power purchases. This paper focused on short-term …
A deep neural multi-model ensemble (DNM2E) framework for modelling groundwater levels over Kerala using dynamic variables
Modelling, predicting, and forecasting hydrological phenomena like groundwater have been
one of the prominent applications of artificial intelligence techniques. Using Multi-Layer …
one of the prominent applications of artificial intelligence techniques. Using Multi-Layer …
[HTML][HTML] Machine learning augmentation of the failure assessment diagram methodology for enhanced tubular structures integrity evaluation
Failure assessment diagrams (FADs) are essential engineering tools for evaluating the
structural integrity of components. However, their widespread application can be limited by …
structural integrity of components. However, their widespread application can be limited by …
Multi-feature-fused generative neural network with Gaussian mixture for multi-step probabilistic wind speed prediction
C Yu, S Fu, ZW Wei, X Zhang, Y Li - Applied Energy, 2024 - Elsevier
Wind speed prediction is a crucial element of effective wind energy utilization, necessitating
the use of probabilistic wind speed prediction to facilitate practical decision-making …
the use of probabilistic wind speed prediction to facilitate practical decision-making …
[PDF][PDF] Forecasting solar irradiance with weather classification and chaotic gravitational search algorithm based wavelet kernel extreme learning machine
AK Pani, N Nayak - International Journal of Renewable Energy …, 2019 - academia.edu
In this work an improved KELM based forecasting model is being proposed, which attains a
specific level of prediction of solar irradiance affecting PV power management. The new …
specific level of prediction of solar irradiance affecting PV power management. The new …