Stanford sky images and PV power generation dataset for solar forecasting related research and applications

Prediction of Solar Power Generated by a power plant using artificial neural networks

Forecasting Solar Power: Analysis of using a LSTM Neural Network

Renewable-Energy-Generation-Forecasting uses AI and statistical models to predict solar, wind, and hydro power output. By analyzing weather patterns, historical data, and grid behavior, it improves energy planning, reduces uncertainty, supports grid stability,

⚡ Power forecasting of 💚 renewable energy power plants is a very active research field, as reliable information about the 🔮 future power generation allow for a safe operation of the power grid and helps to ⤵ minimize the operational costs of these energy sources.

Accompanying scripts and models for paper "Transfer learning strategies for solar power1 forecasting under data scarcity"

This study considers the prediction and forecasting of solar and wind power generation on a country-wide basis for the Greek energy grid.

A project focused on forecasting solar photovoltaic (PV) power generation using regional microclimate data. Implements machine learning models like CatBoost, LightGBM, and XGBoost for predictions, leveraging environmental features like temperature, humidity, wind speed, and solar radiation.

Download and manage sky images for solar power forecasting.

An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting

This repository includes the code for the paper titled as "Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations".

Various machine learning approaches are widely applied for short-term solar power forecasting, which is highly demanded for renewable energy integration and power system planning. However, appropriate selection of machine learning models and data features is a significant challenge. In this study, a framework is developed to quantitatively evaluate various models and feature selection methods, and the best combination for short-term solar power forecasting is discovered. More specifically, the machine learning methods include the random forest, artificial neural network and extreme gradient boosting (XGBoost), and the feature selection techniques include the feature importance and principle component analysis (PCA). All possible combinations of these machine learning and feature selection methods are developed and evaluated for solar power forecasting. The best ensemble of machine learning methods and feature selection techniques is identified for solar power forecasting in Hawaii, US. Simulation results show that the XGBoost method with features selected by the PCA method outperforms the other approaches. In addition, the random forest and XGBoost models have rarely been used for short-term solar forecasting. This framework can be used to select appropriate machine learning approaches for short-term solar power forecasting and the simulation results can be used as a baseline for comparison.

This project is part of my final semester project work for M.Sc degree. The main scope and target here is to forecast annual solar power output from geographic locations from Asia (depends on data) and then reduce the total soft cost incurred . The work is detailed in the documentation and project report provided below.

Solar Power Forecasting

Our project focuses on forecasting photovoltaic (solar) power generation using a hybrid model of Gradient Boosting and LSTM. It predicts solar output with high accuracy, optimizing energy usage, improving grid stability, and enhancing renewable energy integration.

Texas Wind - Turbine About Dataset Problem Statement: The intermittent nature and low control over the wind conditions bring up the same problem to every grid operator in their successful integration to satisfy current demand. In combination with having to predict demand and balance it with the supply, the grid operator now also must predict the availability of wind and solar generation plants in the next hour, day, or week. Apart from holding back the benefits of renewable energy, incorrectly scheduling of wind generation plants may lead to unnecessary reservations, higher costs passed over to the consumer, and use of other more expensive and polluting power resources. Working with real data is challenging due to noise and missing periods. Dataset details: The provided full-year hourly time-series are simulated using the National Renewable Energy Laboratory (NREL) software for a location in Texas, US. It has perfect data completeness, and no noisy data; challenges that hinder forecasting tasks with real datasets and distract from the goal. The dataset contains various weather features which can be analyzed and used as predictors. Colums: Time stamp System power generated | (kW) Wind speed | (m/s) Wind direction | (deg) Pressure | (atm) Air temperature | ('C) Work plan 🤝🤝🤝🤝🤝 1- Data Exploration & Analysis 🤝🤝🤝 2- Building a Machine Learning Model / Predict

Predicting the hourly energy production of a photovoltaic module with an Artificial Neural Network.

An AI-powered app that forecasts solar energy production based on up-to-date weather conditions. Accurate green energy forecasting can help energy grid operators efficiently manage the integration of renewable energy sources, reduce reliance on fossil fuels, and minimize energy waste. 

Solar Power Forecasting

This project focuses on forecasting solar power generation using advanced machine learning models, including XGBoost and Random Forest. The analysis highlights data cleaning, preprocessing, and feature engineering, with Random Forest achieving an R-squared score of 0.91, emphasizing radiation as the most significant predictor.

Machine learning models for forecasting of solar power generation

Solar Power Generation Analysis and Predictive Maintenance using Kaggle Dataset

Machine learning analysis for predicting solar power generation using weather and sensor data from solar plants. This project leverages historical data and machine learning to improve the efficiency of renewable energy systems by optimizing solar energy forecasting.

Forecasting Solar Power for NEOM

The project involves the design of Solar MPPT charge controller using DC/ DC buck converter and Arduino Uno microcontroller. The MPPT controller improves the efficiency of the PV panel by extracting respective maximum power as per the atmospheric conditions. MPPT charge controller will be designed with a 40W PV panel and Perturb & Observe algorithm technique. A Machine Learning based system is developed in order to predict the MPPT power output based on the temperature and irradiance. The main objective of the project is to design solar maximum power point tracking device and its associated forecasting system. When the solar energy is converted to electrical energy the basic parameter that comes into picture is the operating voltage of load and considering the P-V graph of the panel we cannot always achieve the maximum power by directly loading the solar panel thus we need an intermediate device known as MPPT. The forecasting system is built on the sensor data to predict the MPPT power output based on temperature and irradiance. The main objective of the project is to design solar maximum power point tracking device and its associated forecasting system. When the solar energy is converted to electrical energy the basic parameter that comes into picture is the operating voltage of load and considering the P-V graph of the panel we cannot always achieve the maximum power by directly loading the solar panel thus we need an intermediate device known as MPPT. The forecasting system is built on the sensor data to predict the MPPT power output based on temperature and irradiance.

This repository contains the source code for the paper 'Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

Solar Power forecasting for a Solar Power System is a very active research field, as reliable information about the future power generation allow for a safe operation of the power grid and helps to minimize the operational costs. Deep Learning algorithms have shown to be very powerful in forecasting tasks, such as economic time series or speech recognition. Up to now, Deep Learning algorithms have only been applied sparsely for forecasting renewable energy power plants. By using different Deep Learning Algorithms, such as RNN,LSTM with different Feature extraction method like PCA,1D CNN , Auto encoders , My motive is to show the forecast strength of these algorithms compared to a standard MLP and traditional machine learning model in forecasting the energy output of 21 solar power plants.

Deep MultiAttribute Spatial-Temporal Graph Convolutional Recurrent Neural Network for multi-site solar PV power forecasting

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Solar Power Plants Production Estimating On an Hourly Basis