A Proposed Intelligent Model with Optimization Algorithm for Clustering Energy Consumption in Public Buildings

被引:0
作者
Abdelaziz, Ahmed [1 ]
Santos, Vitor [2 ]
Dias, Miguel Sales [3 ]
机构
[1] Univ Nova Lisboa, NOVA Informat Management Sch, P-1070312 Lisbon, Portugal
[2] Higher Technol Inst HTI, Informat Syst Dept, Cairo 44629, Egypt
[3] Inst Univ Lisboa ISCTE IUL, ISTAR, P-1649026 Lisbon, Portugal
关键词
Energy consumption in public buildings; self-organizing map; K-means; genetic algorithm; principal component analysis; SYSTEM; PREDICTION; OCCUPANCY; DESIGN;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Recently ,intelligent applications gained a significant role in the energy management of public buildings due to their ability to enhance energy consumption performance. Energy management of these buildings represents a big challenge due to their unexpected energy consumption characteristics and the deficiency of design guidelines for energy efficiency and sustainability solutions. Therefore, an analysis of energy consumption patterns in public buildings becomes necessary. This reveals the significance of understanding and classifying energy consumption patterns in these buildings. This study seeks to find the optimal intelligent technique for classifying energy consumption of public buildings into levels (e.g., low, medium, and high), find the critical factors that influence energy consumption, and finally, find the scientific rules (If-Then rules) to help decision-makers for determining the energy consumption level in each building. To achieve the objectives of this study, correlation coefficient analysis was used to determine critical factors that influence on energy consumption of public buildings; two intelligent models were used to determine the number of clusters of energy consumption patterns which are Self Organizing Map (SOM) and Batch-SOM based on Principal Component Analysis (PCA). SOM outperforms Batch-SOM in terms of quantization error. The quantization error of SOM and Batch-SOM is 8.97 and 9.24, respectively. K-means with a genetic algorithm were used to predict cluster levels in each building. By analyzing cluster levels, If-Then rules have been extracted, so needs that decision-makers determine the most energy-consuming buildings. In addition, this study helps decision-makers in the energy field to rationalize the consumption of occupants of public buildings in the times that consume the most energy and change energy suppliers to those buildings.
引用
收藏
页码:136 / 152
页数:17
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