The electric energy needs are constantly growing. It is estimated that such demand will increment from 549 quadrillion British thermal unit (Btu), registered in 2012, to 629 quadrillion Btu in 2020. A further increment of 48% is estimated by 2040.
The accurate estimation of the short-term electric energy demand provides several benefits. The economic benefits are evident because this would allow us to allocate only the right amount of resources that are needed in order to produce the amount of energy actually needed to face the actual demand. There are also environmental aspects to consider, since, by producing only the right amount of energy required, the emission of CO2would be reduced as well. In fact, energy efficiency is another relevant goal pursued with these kinds of approaches since the accurate forecasting of electricity demand in public buildings or in industrial plants usually leads to energy savings.
The main objective of the project is to design and develop strategies and tools for analyzing energy consumption data in non-residential and geographically distributed buildings. The secondary objectives of the project are:
- Design a data model and create the database to store the data. Collect, process and analyze data from building sensors.
- Build a predictive model for energy consumption data.
- Build a descriptive model for energy consumption data.
- Develop a dashboard for decision support.
- Validate the project contributions on data from real-life scenarios.
For such purpose, a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay.