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Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings
https://orcid.org/0000-0001-5744-249X
https://orcid.org/0000-0003-3467-4380
https://orcid.org/0000-0003-2890-5090
The extraction and analysis of electricity consumption changing patterns are increasingly important, as they can guide in energy management and efficiency retrofitting. Consequently, it is necessary to extract the laws governing building electricity consumption characteristics. This method should be on an hour-scale and successfully applied online to various buildings. Under these conditions, the method should be as simple as possible to ensure excellent online applications. A matrix model method was developed based on the conventional K-nearest neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method used the slopes of the power consumption curve as the grading standard for the extraction and assessment of the electricity consumption laws. The validation results for seven different buildings with various functions and climate zones, including the mean absolute error, mean absolute percentage error, mean square error, root mean square error, and coefficient of variance, showed that this method met the aforementioned requirements. Moreover, this method can be used for power consumption prediction, which integrated a process of filtering historical data, leading to better accuracy and less data volume than that of other methods that use historical data for prediction.
Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings
https://orcid.org/0000-0001-5744-249X
https://orcid.org/0000-0003-3467-4380
https://orcid.org/0000-0003-2890-5090
The extraction and analysis of electricity consumption changing patterns are increasingly important, as they can guide in energy management and efficiency retrofitting. Consequently, it is necessary to extract the laws governing building electricity consumption characteristics. This method should be on an hour-scale and successfully applied online to various buildings. Under these conditions, the method should be as simple as possible to ensure excellent online applications. A matrix model method was developed based on the conventional K-nearest neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method used the slopes of the power consumption curve as the grading standard for the extraction and assessment of the electricity consumption laws. The validation results for seven different buildings with various functions and climate zones, including the mean absolute error, mean absolute percentage error, mean square error, root mean square error, and coefficient of variance, showed that this method met the aforementioned requirements. Moreover, this method can be used for power consumption prediction, which integrated a process of filtering historical data, leading to better accuracy and less data volume than that of other methods that use historical data for prediction.
Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings
https://orcid.org/0000-0001-5744-249X
https://orcid.org/0000-0003-3467-4380
https://orcid.org/0000-0003-2890-5090
The extraction and analysis of electricity consumption changing patterns are increasingly important, as they can guide in energy management and efficiency retrofitting. Consequently, it is necessary to extract the laws governing building electricity consumption characteristics. This method should be on an hour-scale and successfully applied online to various buildings. Under these conditions, the method should be as simple as possible to ensure excellent online applications. A matrix model method was developed based on the conventional K-nearest neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method used the slopes of the power consumption curve as the grading standard for the extraction and assessment of the electricity consumption laws. The validation results for seven different buildings with various functions and climate zones, including the mean absolute error, mean absolute percentage error, mean square error, root mean square error, and coefficient of variance, showed that this method met the aforementioned requirements. Moreover, this method can be used for power consumption prediction, which integrated a process of filtering historical data, leading to better accuracy and less data volume than that of other methods that use historical data for prediction.
Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings
Zhao, Tianyi (author) / Zhang, Chengyu (author) / Ujeed, Terigele (author) / Ma, Liangdong (author)
Building Services Engineering Research & Technology ; 43 ; 703-724
2022-11-01
22 pages
Article (Journal)
Electronic Resource
English
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