Three-level Classification of Air Conditioning Energy Consumption for Building Energy Management System Using Data Mining Techniques

Chumnanvanichkul, Pornpra; Chirapongsananurak, Pisitpol; Hoonchareon, Naebboon

doi:10.1109/gtdasia.2019.8716004

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…From this perspective, in the last decade, huge efforts have been dedicated to the development of smart cities [ 10 , 11 , 12 ] in an attempt to design more sustainable cities by applying green energies and a high level of smart services [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] with the support of new technologies such as IoT [ 22 , 23 , 24 , 25 , 26 ] and 5G [ 27 , 28 ] and the progress made in AI [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], transport [ 38 ], etc. Within this context, efforts have been dedicated to increasing EE in smart buildings with more intelligent behavior, a high level of comfort and environmentally friendly operation [ 34 , 37 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach

Peña

Biscarri

Personal

et al. 2022

Sensors

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In this paper, an intelligent data analysis method for modeling and optimizing energy efficiency in smart buildings through Data Analytics (DA) is proposed. The objective of this proposal is to provide a Decision Support System (DSS) able to support experts in quantifying and optimizing energy efficiency in smart buildings, as well as reveal insights that support the detection of anomalous behaviors in early stages. Firstly, historical data and Energy Efficiency Indicators (EEIs) of the building are analyzed to extract the knowledge from behavioral patterns of historical data of the building. Then, using this knowledge, a classification method to compare days with different features, seasons and other characteristics is proposed. The resulting clusters are further analyzed, inferring key features to predict and quantify energy efficiency on days with similar features but with potentially different behaviors. Finally, the results reveal some insights able to highlight inefficiencies and correlate anomalous behaviors with EE in the smart building. The approach proposed in this work was tested on the BlueNet building and also integrated with Eugene, a commercial EE tool for optimizing energy consumption in smart buildings.

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Section: Introductionmentioning

confidence: 99%

Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach

Peña

Biscarri

Personal

et al. 2022

Sensors

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show abstract

“…Many researchers did a lot of studies about that. Some of them present a management system for AC using data mining framework and based on a combination of unsupervised [15]. Other studied the controlling of AC regarding humidity using A hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) [16].…”

Section: Introductionmentioning

confidence: 99%

Air Conditioning Consumption Optimization Based on CO2 Concentration Level

Khorram

Zheiry

Faria

et al. 2019

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)

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Nowadays, energy consumption increasing is a big concern for many countries around the world. Disadvantages and consequences of fossil fuels for the environment caused a lot of efforts to invest in renewable energy resources and programs to optimize energy consumption. All types of buildings are the major consumers of electric power. Therefore, buildings can be considered as good options for implementing optimization algorithms, assuming that they are equipped to required infrastructures. Air conditioners are flexible loads that can be directly controlled by optimization programs. This paper presents a particle swarm optimization algorithm to minimize the power consumption of the air conditioners based on the carbon dioxide concentration level. The algorithm considers the thermal comfort of users with defining restrictions. The case study of the paper proposes two scenarios with real monitored data of a building. The result of the paper shows the obtained results of the algorithm and makes the comparison of two scenarios.

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Three-level Classification of Air Conditioning Energy Consumption for Building Energy Management System Using Data Mining Techniques

Cited by 2 publications

References 6 publications

Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach

Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach

Air Conditioning Consumption Optimization Based on CO2 Concentration Level

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