In the actual climate change emergency situation several transformations and redefinitions in
the systems are required. The thesis focuses in the end user of the electricity sector,
specifically in the demand side management strategies. The development of a data pipeline
from scratch for a Home Energy Management System is presented, with detailed
explanations of the data fluxes, treatment, storage, visualization, analysis and conversion.
Also the code that runs the system is provided and explained for better understanding. It is
programmed in python, an open source language with powerful data libraries.
The resulting program defines a standalone data collection entity that collects, treats, stores
and forecasts diverse data provided by assets located in a household grid. Those assets are
electrical loads, a photovoltaic array, an electric vehicle and are completed with external
data sources like Spanish electricity market retail price, weather stations and specific PV
libraries.
All the stored data turns to be a useful resource for further analysis such forecasting and
visualization purposes, real demand and production curves are plotted and compared with
calculated predictions of generation and consumption. This information is refined and stored
thanks to a database manager module developed that eases also the process of data
extraction. The pipeline is ready to correctly feed the optimization engine of a HEMS.
In addition to it the document provides useful information in REST APIs communications with
known platforms such Nissan Kamereon, Fronius devices, OpenWeatherMap source and
ESIOS database. There are also object oriented programming tips, explanations and
examples in Python. It accounts with photovoltaic modelization using PVlib and its powerful
tools and finally data storage and analysis with pandas library and pickle files.
