Project Coordinator: PhD. Eng. Eleonora TUDORA – Scientific Researcher III

General objectives of the project

The project has the following specific objectives:

(1) to create a rigorous and replicable system for testing advanced technological solutions (IoT, Big Data, cloud computing, predictive analytics), having applicability in the field of estimating energy consumption in buildings;

(2) to describe and predict energy consumption in buildings using advanced prediction techniques;

(3) to develop and test algorithms, methods and software tools for analysis, modelling and prediction of time series in the field of energy consumption in buildings;

(4) to develop and test algorithms, methods, and software tools for data analysis, including presentation of results in various formats;

(5) to carry out experiments, case studies and scenarios, using simulated data and/or real data, which demonstrate the functionality and usefulness of the technologies used.

Project description

The "Intelligent system for predicting energy consumption in buildings (PRECONERG)" project aims to develop, test and validate an energy consumption prediction system using advanced prediction techniques with the help of energy consumption prediction models based on artificial intelligence. The system will contain smart devices for collecting energy parameters, environmental sensors and a cloud platform for data storage and aggregation, including an interface for presenting the obtained results. The system will integrate advanced Machine Learning capabilities to identify energy saving opportunities while reducing reliance on human intervention to identify and resolve operational issues. In this context, the aim of the project is to demonstrate the positive transformation of the way in which the estimation and prediction of energy consumption in buildings is carried out, using an intelligent prediction system.

Results

The estimated results of the project are:

• Intelligent energy consumption prediction system that provides:
  • Data acquisition services related to the energy consumption of buildings,
  • Predictive models based on Artificial Intelligence for the prediction of energy consumption in buildings;
• Articles in journals and international and national conference proceedings indexed: 14 (indexed Clarivate Analytics);
• Licensing applications:1;
• Project presentation website.