Objective: Utilize ivy to develop a machine learning model that forecasts energy consumption in a building or region. By analyzing historical energy usage data, weather patterns, and other pertinent factors, this project aims to facilitate the optimization of energy usage and contribute to cost reduction efforts. This initiative is particularly relevant for those interested in sustainability, urban planning, and smart infrastructure development.
Task Details:
Dataset: The project will employ the SmartMeter Energy Use Data in London Households dataset, available at London SmartMeter Energy Use Dataset. This dataset provides detailed energy usage readings in a granular format, coupled with weather conditions and other potentially influential factors, offering a comprehensive base for predictive modeling.
Expected Output: Participants are required to submit a Jupyter notebook that thoroughly outlines the data preprocessing steps, exploratory data analysis, feature engineering, model development, training, and evaluation phases. Additionally, the submission should include the final trained model files.
Submission Directory: Please ensure that your completed Jupyter notebook and any associated model files are placed within the Contributor_demos/Energy Consumption Forecasting subdirectory of the unifyai/demos repository.
How to Contribute:
Begin by forking the unifyai/demos repository to your own GitHub account.
Clone the forked repository to your local system.
Create a new branch dedicated to your work on the Energy Consumption Forecasting demo.
Develop your forecasting model, ensuring to document each step and decision in the Jupyter notebook comprehensively.
Save your notebook and any related model files in the specified Contributor_demos/Energy Consumption Forecasting directory.
After completing your model, push the changes to your forked repository.
Open a Pull Request (PR) to the unifyai/demos repository with a clear and descriptive title, like "Energy Consumption Forecasting Demo Submission".
Contribution Guidelines:
Your code should be well-documented to ensure it is accessible and understandable to others.
Include a summary of your approach, key insights, and any challenges you encountered in the PR description to provide context to reviewers and future contributors.
Objective: Utilize ivy to develop a machine learning model that forecasts energy consumption in a building or region. By analyzing historical energy usage data, weather patterns, and other pertinent factors, this project aims to facilitate the optimization of energy usage and contribute to cost reduction efforts. This initiative is particularly relevant for those interested in sustainability, urban planning, and smart infrastructure development.
Task Details:
Dataset: The project will employ the SmartMeter Energy Use Data in London Households dataset, available at London SmartMeter Energy Use Dataset. This dataset provides detailed energy usage readings in a granular format, coupled with weather conditions and other potentially influential factors, offering a comprehensive base for predictive modeling.
Expected Output: Participants are required to submit a Jupyter notebook that thoroughly outlines the data preprocessing steps, exploratory data analysis, feature engineering, model development, training, and evaluation phases. Additionally, the submission should include the final trained model files.
Submission Directory: Please ensure that your completed Jupyter notebook and any associated model files are placed within the
Contributor_demos/Energy Consumption Forecastingsubdirectory of theunifyai/demosrepository.How to Contribute:
unifyai/demosrepository to your own GitHub account.Contributor_demos/Energy Consumption Forecastingdirectory.unifyai/demosrepository with a clear and descriptive title, like "Energy Consumption Forecasting Demo Submission".Contribution Guidelines: