Data acquisition pipeline design and HEMS integration for prosumers' economic savings

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