GitHub - DebbieIbukun/My-Project: 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.

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Name		Name	Last commit message	Last commit date
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Abalone_Age.ipynb		Abalone_Age.ipynb
BDG2 Project Abstract		BDG2 Project Abstract
Citibikes Trip		Citibikes Trip
Citibikes code.sql		Citibikes code.sql
Deb_Project_Genome.ipynb		Deb_Project_Genome.ipynb
Household power consumption codes.ipynb		Household power consumption codes.ipynb
My Excel Project Learning.htm		My Excel Project Learning.htm
Predicting Household Electricity Consumption		Predicting Household Electricity Consumption
Predicting age of an Abalone		Predicting age of an Abalone
README.md		README.md
embedded_dashboard.html		embedded_dashboard.html

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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.

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