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Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities
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2021 | International Journal of Information Management | Citations: 0
Authors: Zekić-Sušac, Marijana; Mitrović, Saša; Has, Adela

Abstract: Energy efficiency of public sector is an important issue in the context of smart ... Expand

Abstract: Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment. Collapse

Semantic filters: parametric test goal programming

Topics: internet of things smart city mobile system decision making big data

Methods: machine learning machine learning method random forest classification decision tree classification computational algorithm
Classification by vertical and cutting multi-hyperplane decision tree induction
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2010 | Decision Support Systems | Citations: 0
Authors: Better, Marco; Glover, Fred; Samorani, Michele

Abstract: Two-group classification is a key task in decision making and data mining applic ... Expand

Abstract: Two-group classification is a key task in decision making and data mining applications. We introduce two new mixed integer programming formulations that make use of multiple separating hyperplanes. They represent a generalization of previous piecewise-linear models that embed rules having the form of hyperplanes, which are used to successively separate the two groups. In fact, the classifiers obtained are particular types of decision trees which are allowed to grow in depth and not in width. Computational results show that our models achieve better classification accuracy in less time than previous approaches. Collapse

Semantic filters: parametric test goal programming

Topics: database system data management missing data workstation

Methods: support vector machine decision tree classification goal programming trade-off analysis method experiment

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Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities

Classification by vertical and cutting multi-hyperplane decision tree induction