The project deals with developing a methodological framework for efficient energy management by intelligent data analytics with the focus on machine learning methods and simulation modelling. The research aims to scientifically contribute the realization of European Commission directives about reducing greenhouse gas emissions, increasing energy efficiency and using 20% of energy consumption from renewable energy resources until 2020.
In Croatia and in other EU countries there are Strategies of energy development as well as National plans of energy efficiency, which quantify and control the objectives of reducing immediate energy consumption. However, the data on energy efficiency have not been scientifically analyzed enough for the purpose of efficient management of energy consumption and cost reduction, while there is a lack of research that use machine learning methods to more precisely detect interdependence among variables, prediction of payback period and other analytics. The purpose of this project is to conduct an intelligent data analysis on public buildings energy efficiency, and to suggest methods and models that will enable better planning of national energy policy and energy cost in public sector buildings.
The project suggests a methodological framework based on machine learning methods such as neural networks, decision trees, cluster analysis, association rules, and other methods that could be used for intelligent efficient management of energy consumption and energy supply cost. The methods will be tested on data that describe energetic characteristics of buildings, on the data used in the process of planning and implementing measures for improving energetic characteristics of buildings, and on the data describing their energy consumption. In relation to the energy supply, the project will be focused on data describing the supply chain of natural gas, as one of the major energy source in the public sector, aiming to find possible improvements in its efficiency.
The assumption is that a combined usage of various machine learning methods as well as simulation modelling can lead to lower energy consumption, higher efficiency of energy supply chain management, lower energy costs, more accurate evaluation of investment payback period, and better environment protection by lowering emission of harmful gasses. In order to develop such a methodological framework, it is necessary to investigate which methods of intelligent data analytics produce successful models for explaining and predicting energy consumption and suply, and how to integrate them, which is the topic of this project.
Supported by: Hrvatska zaklada za znanost, Ekonomski fakultet u Osijeku, Sveučilišni računski centar Sveučilišta u Zagrebu Srce, Odjel za matematiku Osijek, Građevinski fakultet Osijek, HEP Plin d.o.o.