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Patterns and influencing factors analysis of electricity consumption in university dormitories based on balance point temperatures
https://orcid.org/0000-0002-1386-9413
https://orcid.org/0000-0002-6720-9541
Abstract The high energy density of university dormitories deserves attention for its huge energy-saving potential. To investigate the patterns and influencing factors of electricity consumption in university dormitories, this study presents a methodology that combines clustering and covariance method based on balancing point temperatures. The dataset of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The main results are as follows. (1) Using the Joinpoint Regression (JPR) method, two balance point temperatures, 20.1 °C and 26.6 °C, that affect electricity consumption trend are identified. (2) Through cluster analysis of 3D data, the dormitories are grouped into three electricity use patterns. The phenomenon of the shift in electricity consumption patterns from the fall semester to the spring semester is discovered. (3) The impact of 8 non-behavioral factors on electricity consumption is investigated through covariance analysis. The results show that gender is the most influential factor in base electricity consumption, while three factors have the greatest impact on cooling electricity consumption: floor, occupancy and room location. (4) Based on the main influential factors of room cooling load, a hot season daily cooling electricity subsidy strategy is proposed to balance the difference in cooling electricity bills for different rooms. This study provides reliable information to support the design of potential electricity saving and management measures for dormitory buildings.
Graphical abstract Display Omitted
Highlights The actual daily electricity consumption data of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The balance point temperatures affecting the electricity consumption trend are identified using the Joinpoint Regression model. The dormitory electricity consumption patterns and their shifts are studied using k-means clustering algorithm of 3D data. The effect of 8 non-behavioral factors on the base and cooling electricity consumption are quantified using covariance methods. A hot season daily cooling electricity subsidy strategy is proposed.
Patterns and influencing factors analysis of electricity consumption in university dormitories based on balance point temperatures
https://orcid.org/0000-0002-1386-9413
https://orcid.org/0000-0002-6720-9541
Abstract The high energy density of university dormitories deserves attention for its huge energy-saving potential. To investigate the patterns and influencing factors of electricity consumption in university dormitories, this study presents a methodology that combines clustering and covariance method based on balancing point temperatures. The dataset of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The main results are as follows. (1) Using the Joinpoint Regression (JPR) method, two balance point temperatures, 20.1 °C and 26.6 °C, that affect electricity consumption trend are identified. (2) Through cluster analysis of 3D data, the dormitories are grouped into three electricity use patterns. The phenomenon of the shift in electricity consumption patterns from the fall semester to the spring semester is discovered. (3) The impact of 8 non-behavioral factors on electricity consumption is investigated through covariance analysis. The results show that gender is the most influential factor in base electricity consumption, while three factors have the greatest impact on cooling electricity consumption: floor, occupancy and room location. (4) Based on the main influential factors of room cooling load, a hot season daily cooling electricity subsidy strategy is proposed to balance the difference in cooling electricity bills for different rooms. This study provides reliable information to support the design of potential electricity saving and management measures for dormitory buildings.
Graphical abstract Display Omitted
Highlights The actual daily electricity consumption data of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The balance point temperatures affecting the electricity consumption trend are identified using the Joinpoint Regression model. The dormitory electricity consumption patterns and their shifts are studied using k-means clustering algorithm of 3D data. The effect of 8 non-behavioral factors on the base and cooling electricity consumption are quantified using covariance methods. A hot season daily cooling electricity subsidy strategy is proposed.
Patterns and influencing factors analysis of electricity consumption in university dormitories based on balance point temperatures
https://orcid.org/0000-0002-1386-9413
https://orcid.org/0000-0002-6720-9541
Abstract The high energy density of university dormitories deserves attention for its huge energy-saving potential. To investigate the patterns and influencing factors of electricity consumption in university dormitories, this study presents a methodology that combines clustering and covariance method based on balancing point temperatures. The dataset of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The main results are as follows. (1) Using the Joinpoint Regression (JPR) method, two balance point temperatures, 20.1 °C and 26.6 °C, that affect electricity consumption trend are identified. (2) Through cluster analysis of 3D data, the dormitories are grouped into three electricity use patterns. The phenomenon of the shift in electricity consumption patterns from the fall semester to the spring semester is discovered. (3) The impact of 8 non-behavioral factors on electricity consumption is investigated through covariance analysis. The results show that gender is the most influential factor in base electricity consumption, while three factors have the greatest impact on cooling electricity consumption: floor, occupancy and room location. (4) Based on the main influential factors of room cooling load, a hot season daily cooling electricity subsidy strategy is proposed to balance the difference in cooling electricity bills for different rooms. This study provides reliable information to support the design of potential electricity saving and management measures for dormitory buildings.
Graphical abstract Display Omitted
Highlights The actual daily electricity consumption data of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The balance point temperatures affecting the electricity consumption trend are identified using the Joinpoint Regression model. The dormitory electricity consumption patterns and their shifts are studied using k-means clustering algorithm of 3D data. The effect of 8 non-behavioral factors on the base and cooling electricity consumption are quantified using covariance methods. A hot season daily cooling electricity subsidy strategy is proposed.
Patterns and influencing factors analysis of electricity consumption in university dormitories based on balance point temperatures
Yang, Hao (author) / Ran, Maoyu (author) / Zeng, Pengyuan (author)
Building and Environment ; 228
2022-10-04
Article (Journal)
Electronic Resource
English
UB Braunschweig | 1962
Foothill Dormitories: University of California, Berkeley/William Turnbull Associates
| British Library Online Contents | 1993