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This repository contains codes developed in Python which deals with smart meter analytics. Building consumption dataset from Pecan Street Dataport was obtained along with temperature and irradiance data. The dataset was used to build machine learning models using linear regression, random forest deicision tree, Neural networks and Support vector…

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MachineLearning-Smart-Meter-Analytics (2018)

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This repository contains the Jupyter notebooks of the intriguing physical constants ratios that are at play in Advanced Metering Infrstructure Analytics of [Jupyter-Notebook-MachineLearning-Smart-Meter-Analytics(2018)](https://www.packtpub.com/big-data-and-smart-meter/-first-e/ https://zenodo.org/badge/latestdoi/), by Ami-Solution, Packt Publishing.

This repository is read-only: the source files are on the Jupyter-Notebook-MachineLearning-Smart-Meter-Analytics repository.

Running the code in the cloud

With Binder, you can run most of the Jupyter notebooks directly from your web browser without installing anything. Just click on the launch binder button above. A temporary Jupyter Notebook server with all dependencies will be automatically launched in the cloud. It is not persistent: all your changes will be lost after some time.

Master Thesis Energy Engineering Course

This project was developed with the purpose of using it as a master dissertation for Energy engineering course at Politecnico di Milano under the Data Analytics and Optimization for Energy applications Lab

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This repository contains codes developed in Python which deals with smart meter analytics. Building consumption dataset from Pecan Street Dataport was obtained along with temperature and irradiance data. The dataset was used to build machine learning models using linear regression, random forest deicision tree, Neural networks and Support vector…

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