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PREDICTION OF SOLAR POWER GENERATION BASED ON WEATHER FORECAST

Ansika Babu

Ansika Babu

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Solar energy is abundant, sustainable, and renewable, making it an ideal source of energy world wide. This project aims in building a model to estimate the cost and power generated by solar power in a particular region by weather forecast and motivate to adopt these methods for a better living. ...learn more

Project status: Under Development

oneAPI

Intel Technologies
oneAPI, Intel Python

Docs/PDFs [1]Code Samples [1]

Overview / Usage

Our objective is to combat the lack of awareness among homeowners about the substantial financial benefits attainable through the installation of solar panels. By highlighting the potential for significant cost savings and even earnings, we aim to bridge the information gap and encourage wider adoption of solar energy solutions, paving the way for a more sustainable and economically advantageous future for everyone.

Methodology / Approach

Intel OneAPI is a comprehensive software toolkit designed to simplify and accelerate the development of high-performance, data-centric applications across diverse computing architectures. It provides a unified programming model that allows developers to create code that can run on CPUs, GPUs, FPGAs, and other accelerators without requiring separate codebases. Intel OneAPI includes optimized libraries, frameworks, and tools for various workloads such as AI, HPC, and edge computing, enabling developers to harness the full potential of heterogeneous computing environments. This approach streamlines development, improves efficiency, and maximizes performance for a wide range of applications in today's complex computing landscape.

USE OF ONEDAL IN OUR PROJECT

  • Data Preparation: oneDAL can be used for efficient preprocessing of historical weather data and solar irradiance information. It provides tools for data cleaning, transformation, and feature engineering, ensuring that the input data for solar power estimation is accurate and relevant.
  • Statistical Analysis: oneDAL's statistical algorithms can assist in identifying patterns and trends in weather data, helping to improve the accuracy of solar energy predictions.
  • Parallel Processing: The parallel processing capabilities of oneDAL can be employed to handle large datasets and optimize computation times, which is crucial for timely analysis in this project.

Technologies Used

Technologies used :

  • Intel Open API
  • Python
  • Streamlit
  • Weather API
  • Geolocation API

Documents and Presentations

Project_Proposal_.pdf

Repository

https://github.com/Julian-Graf/Solar-Earnings-Estimator

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