Accurate prediction of photovoltaic (PV) power depends on meteorological forecasting techniques, which is crucial for safe operation of power system.In order to improve ultra-short-term PV power prediction, a hybrid deep learning model that integrates meteorological models and station measurement data is proposed.

This deep learning project predicts satellite images of tropical storms & wind speeds accurately. Using seq2seq models, CNNs, & LSTMs, it forecasts future storm visuals & wind speeds. With user-friendly modules, it's a valuable resource for meteorology & climate science.

Deep Learning for Meteorological Forecasting: Advanced Weather Prediction Using LSTM Networks

A novel intelligent deep learning predictive model for meteorological drought forecasting

A repository for creating near real time meteorological satellite imagery for the purpose of visualizing outputs created by deep learning forecasts for tropical storms and hurricanes.

Deep learning system for hyper-local weather forecasting across all 47 Kenya counties. Trains and compares LSTM, GRU & ConvLSTM models on 10 years of historical meteorological data. Interactive 7-day forecast map built with Streamlit & Folium.

This is a repository that contains the codes and the some of the processed data used for the undergraduate thesis titled "Forecasting Air Pollution of Dhaka City with Meteorological Parameters Using Deep Learning-Based Approach". For more details please contact me at: rvnrafsan@gmail.com

Machine learning and deep learning for weather forecasting using Greek meteorological data.

Built a transformer-based deep learning model for time-series weather forecasting using historical meteorological data.

A Hybrid CNN–GRU deep learning model for short-term temperature forecasting using historical meteorological data, deployed with a Streamlit interface for real-time prediction of next-day temperatures.

A deep learning-based multivariate time series project comparing Transformer, TCN, N-BEATS, TFT, TimeLLM, and TimesFM architectures for forecasting flight arrival delays using operational and meteorological features.

A multimodal deep learning system for weather forecasting using spatio-temporal graph representations of meteorological stations. The project integrates data from multiple sources to predict local conditions and short-term trends.

Weather-augmented multi-horizon electricity load forecasting for Ireland using statistical (SARIMAX), machine learning (Gradient Boosting), and deep probabilistic (DeepAR) models with ENTSO-E load data and NASA POWER meteorological covariates.

Deep learning system for hyper-local weather forecasting across all 47 Kenya counties. Trains and compares LSTM, GRU & ConvLSTM models on 10 years of historical meteorological data. Interactive 7-day forecast map built with Streamlit & Folium.

Forecasting key meteorological parameters for aviation safety using ERA5 reanalysis data. Includes implementation of SARIMAX and deep learning models (LSTM, Encoder–Decoder, Attention-based LSTM) applied to Nouasseur Airport as a case study.

A Deep Learning approach for accurate rainfall estimation using meteorological data. It optimizes model selection, hyperparameters, and feature engineering, supporting inputs like satellite imagery and sensor data. Ideal for weather forecasting, climate research, and disaster management.

This study presents a robust data-driven framework for forecasting energy consumption across multiple building sites using deep learning, specifically Long Short-Term Memory (LSTM) networks. Leveraging the publicly available BDG2 dataset, which integrates hourly electricity consumption, meteorological conditions, and building metadata.

An Air Quality Index (AQI) prediction project uses machine learning or deep learning techniques to forecast future air pollution levels based on historical data, pollutant concentrations, and meteorological factors. The goal is to provide timely, actionable information for public health and environmental management.

This repository explores the application of various deep learning models, including LSTM, RNN, and GRU, to predict rainfall in Australia. By leveraging a comprehensive meteorological dataset from Kaggle, the study aims to enhance weather forecasting accuracy, which is crucial for agriculture, water resource management, and disaster prevention.

Air pollution is one of the most concerning problems world- wide. It leads to the necessary time series forecasting particulate mat- ter (PM) concentrations. In this study, we propose an effective way to improve the results of PM10 and PM25 forecasting tasks by combin- ing meteorological features and timestamp information using datasets from Hanoi, Vietnam. By conducting five deep learning models, MLP, 1D-CNN, LSTM, Bi-LSTM, and Stacked LSTM, the experimental re- sults show that adding more timestamps information helped improve the results in 68% of cases compared to other methods. Furthermore, the Vanilla LSTM model with combined features will give better results in the long-term forecast than the other. We plan to extend our ap- proach by continuing to combine more features from multiple sources and applying more robust models for the main problem.