Methodological Framework for Efficient Energy Management by Intelligent Data Analytics


Faculty of Economics in Osijek

Trg Lj. Gaja 7
31000 Osijek


Principal Investigator (PI): Marijana Zekić-Sušac, PhD

Name of the PI’s host institution for the project: Josip Juraj Strossmayer University of Osijek, Faculty of Economics in Osijek

Project full title: Methodological Framework for Efficient Energy Management by Intelligent Data Analytics

Project number: IP-2016-06-8350

Project duration in months: 48

Project funded by: Croatian Science Foundation


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.

Methodological framework developed in this project will have the generalization ability, therefore with minor modifications it could be used for other types of buildings, such as residential buildings, and also in any geographical area. The guidelines for model usage in Croatia and also in other countries will be given, which provides international importance to the project. The project offers a novel multi-methodological approach which could be implemented in an information system and contribute to more efficient energy management and better environment protection. Besides, the project provides a starting basis for developing a data laboratory that could be used for intelligent data analytics in the area of energy efficiency and in other areas after completion of the project, for generating new knowledge from data.

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