Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
The integration of large-scale wind power introduces issues in modern power systems
operations due to its strong randomness and volatility. These issues can be resolved via …
operations due to its strong randomness and volatility. These issues can be resolved via …
Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station
Non-predictive or inaccurate weather forecasting can severely impact the community of
users such as farmers. Numerical weather prediction models run in major weather …
users such as farmers. Numerical weather prediction models run in major weather …
Deep learning-based effective fine-grained weather forecasting model
It is well-known that numerical weather prediction (NWP) models require considerable
computer power to solve complex mathematical equations to obtain a forecast based on …
computer power to solve complex mathematical equations to obtain a forecast based on …
A selective ensemble approach for accuracy improvement and computational load reduction in ann-based pv power forecasting
Day-ahead power forecasting is an effective way to deal with the challenges of increased
penetration of photovoltaic power into the electric grid, due to its non-programmable nature …
penetration of photovoltaic power into the electric grid, due to its non-programmable nature …
Wireless sensor network and deep learning for prediction greenhouse environments
A Ali, HS Hassanein - 2019 International conference on smart …, 2019 - ieeexplore.ieee.org
Greenhouses are anti-seasonal. Particularly in regions with adverse climate conditions.
Controlling, monitoring and predicting a greenhouse is important to allow optimal growth …
Controlling, monitoring and predicting a greenhouse is important to allow optimal growth …
A novel machine learning based approach for rainfall prediction
N Solanki, G Panchal - … Technology for Intelligent Systems (ICTIS 2017) …, 2018 - Springer
The climate changes effortlessly nowadays, prediction of climate is very hard. However, the
forecasting mechanism is the vital process. It is also a valuable thing as it is the important …
forecasting mechanism is the vital process. It is also a valuable thing as it is the important …
Rainfall prediction using Artificial Neural network in the South Pacific region
A Chand, R Nand - 2019 IEEE Asia-Pacific Conference on …, 2019 - ieeexplore.ieee.org
Rainfall prediction is one of the most important and at the same time challenging task.
Meteorologists can predict weather patterns such as rainfall based on atmospheric …
Meteorologists can predict weather patterns such as rainfall based on atmospheric …
Analyzing predictive ability of artificial neural network–based short-term forecasting algorithms for temperature and wind speed
JA Sunglee, Y Beeharry - Artificial Intelligence for Renewable Energy …, 2022 - Elsevier
Neural networks are well known for solving complex predictive problems in different fields.
This project uses an artificial neural network (ANN) for short-term forecasting of weather …
This project uses an artificial neural network (ANN) for short-term forecasting of weather …
The Role of Machine Learning in Big Data Analytics: Current Practices and Challenges
HA Duran-Limon, A Chavoya… - … Methodologies for Big …, 2023 - Springer
A massive amount of data is generated at an ever-increasing rate. Social media, mobile
phones, sensors, and medical imaging, among others, are examples of data sources. An …
phones, sensors, and medical imaging, among others, are examples of data sources. An …
[PDF][PDF] Industrial financial forecasting using long short-term memory recurrent neural networks
This research deals with the industrial financial forecasting in order to calculate the yearly
expenditure of the organization. Forecasting helps in estimation of the future trends and …
expenditure of the organization. Forecasting helps in estimation of the future trends and …