In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Artificial intelligence has become the backbone of modern decision sup port systems. This is why a complex method for finding solutions for neuro fuzzy expert systems has been deve loped. The proposed complex meth od is based on a mathematical model for the analysis of the operational si tuation. The model makes it pos sible to determine the parameters of the analysis of the operational sit uation, their influence on the qual ity of assessment of the operation al situation and to determine their number with units of measurement. An increase in the efficiency of infor mation processing (error reduction) of the assessment is achieved by the use of evolving neurofuzzy artificial neural networks. Training of evolv ing neurofuzzy artificial neural net works is carried out by training not only synaptic weights of the artifi cial neural network, the type, parame ters of the membership function, but also by applying the procedure for reducing the dimension of the feature space. The efficiency of information processing is also achieved by train ing the architecture of artificial neu ral networks; accounting for the type of uncertainty in the information to be assessed; work with both clear and fuzzy data. We achieved a reduction in computational complexity while mak ing decisions; the absence of errors in training artificial neural networks as a result of processing information entering the input of artificial neural networks. The analysis of the opera tional situation as a whole occurs due to the improved clustering procedure, which allows working with both static and dynamic data. The proposed com plex method was tested on the example of assessing the state of the operatio nal situation. The mentioned example showed an increase in assessment effi ciency at the level of 20-25 % in terms of information processing efficiency

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“…-to increase the efficiency of information processing, evolving artificial neural networks with the algorithm of their training are used [24];…”

Section: Discussionmentioning

confidence: 99%

Development of a complex method for finding a solution for neuro-fuzzy expert systems

Sova

Shyshatskyi

Malitskyi

et al. 2020

EEJET

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“…In [5], an approach to in-depth analysis of various types of data affecting the energy efficiency of buildings based on the representation of the hierarchy of factors in the form of a multidimensional cube with different levels of abstraction is proposed. This approach allows building a multi-level description of the object, but does not take into account the uncertainty about the state of the monitoring object, which does not allow a full assessment of its condition.…”

Section: The Complex Methodology For Processing Heterogeneous Data Inmentioning

confidence: 99%

Development of complex methodology of processing heterogeneous data in intelligent decision support systems

Zuiev¹,

Zhyvotovskyi²,

Zvieriev³

et al. 2020

EEJET

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processing of data coming from various technical devices of information extraction [1, 2]. The main advantage of information retrieval technologies over other information technologies is the ability to combine different types of information from different da-1. Introduction The rapid development of the number of information sources in the world significantly complicates the process of data extraction and necessitates their integration and

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“…These two methods rely on multiple linear regression for predictive modelling and the feature selection is achieved by adding a L1 or L2 regularisation terms to the objective function (Guyon and Elisseeff, 2003;Fan et al, 2014). Additionally, decision tree is often used as the base learner for regularisation-based embedded methods, e.g., RF and C5.0 (Chae et al, 2016;Noh et al, 2017). Chae et al employed RF algorithm for feature selection.…”

Section: Feature Selection Techniquesmentioning

confidence: 99%

A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data

Fan

Chen

Wang³

et al. 2021

Front. Energy Res.

223

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The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management. Data preprocessing serves as the foundation for valid data analyses. It is an indispensable step in building operational data analysis considering the intrinsic complexity of building operations and deficiencies in data quality. Data preprocessing refers to a set of techniques for enhancing the quality of the raw data, such as outlier removal and missing value imputation. This article serves as a comprehensive review of data preprocessing techniques for analysing massive building operational data. A wide variety of data preprocessing techniques are summarised in terms of their applications in missing value imputation, outlier detection, data reduction, data scaling, data transformation, and data partitioning. In addition, three state-of-the-art data science techniques are proposed to tackle practical data challenges in the building field, i.e., data augmentation, transfer learning, and semi-supervised learning. In-depth discussions have been presented to describe the pros and cons of existing preprocessing methods, possible directions for future research and potential applications in smart building energy management. The research outcomes are helpful for the development of data-driven research in the building field.

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In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Cited by 30 publications

References 27 publications

Development of a complex method for finding a solution for neuro-fuzzy expert systems

Development of a complex method for finding a solution for neuro-fuzzy expert systems

Development of complex methodology of processing heterogeneous data in intelligent decision support systems

A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data

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