Energy Consumption Characterization in University Campus Microgrid Based on Power Data Analysis

Hajjaji, Imad; Alami, Hassan El; Alami, Rafiq El; Dahmouni, Hamza

doi:10.1109/ficloud57274.2022.00022

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The objective of training a neural network on a dataset is to accurately capture the underlying correlation between the time series input and continuous-valued output variables. The training method for evaluating the neural fit using ten (10) layers yielded a moderate linear correlation, as evidenced by six (6) validation checks, between the anticipated power (P) and the actual dry bulb temperature (T) in classrooms and laboratory rooms. There is a higher correlation coefficient that indicates a strong linear association between these metrics in computer rooms and office areas.…”

Section: Neural Network Fittingmentioning

confidence: 95%

“…Related researches include [3] using dynamic and static and hybrid data analysis in buildings, Related research such as [4] using K-means and [5] using kshape and random forest (KS-RF), to classify users according to power consumption behavior based on grid demand. A seasonal approach of educational building consumption from daily usage was limited to descriptive analysis through data cleaning and visualization [6], while [7] used Independent Component Analysis (ICA) to determine factors affecting consumption. A comprehensive review of building energy consumption prediction employing various neural network and regression methods, published between 2015 to 2022, was conducted by Borowski and Zwolińska [8].…”

Section: Introductionmentioning

confidence: 99%

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Power Consumption Modeling of Airconditioning Units in an Educational Building Using Polynomial and Neural Network Fitting Techniques

Brucal,

Africa,

Navea

et al. 2024

Preprint

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There are several challenges on how to attain energy efficiency while maintaining balance among factors affecting energy consumption such as power rating and temperature setpoints of cooling units, room temperature and humidity, and indoor air quality (IAQ). A real-time energy and IAQ monitoring system were installed in an educational building to profile the operational power consumption of inverter-based air condition unit (ACU) installed in each room. Polynomial curve and neural fitting regression analysis were applied to the real-time power consumption, indoor temperature and humidity, and carbon dioxide (CO2) level data. The derived models were able to provide the stabilizing indoor thermal and air conditions of the room to reach steady state ACU power consumption. These collected data and calculated parameters can be used to define rules in automating control of cooling appliances for an efficient energy utilization. The regression approaches, using real-time data, have determined the influence of indoor heat conditions and carbon dioxide levels on ACU power consumption. These parameters were utilized to establish consistent values for temperature, humidity, and carbon dioxide levels under stable settings of inverter-based air conditioning units.

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Section: Neural Network Fittingmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Power Consumption Modeling of Airconditioning Units in an Educational Building Using Polynomial and Neural Network Fitting Techniques

Brucal,

Africa,

Navea

et al. 2024

Preprint

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show abstract

“…While the existing body of literature has delved into energy label awareness and consumer attitudes in diverse settings [51][52][53][54][55], a more specific exploration of energy consumption attitudes among university employees is relatively scarce. Recognizing this gap, we aim to contribute to the existing knowledge by focusing on academic and administrative staff's distinctive perspectives and behaviors within the university context.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Labels and Consumer Attitudes: A Study among University Staff

Durmus Senyapar,

Duzgun,

Boran

2024

Sustainability

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This study investigates energy label awareness among Gazi University staff, exploring the influence of demographic factors on attitudes and perceptions. Using the Theory of Planned Behavior (TPB), the research examines individual attitudes, subjective norms, and perceived behavioral control in shaping energy efficiency awareness. A cross-sectional survey of academic and administrative staff reveals significant differences in awareness across demographic groups. Gender-specific variations in attitudes towards energy-efficient products are evident, with the greatest difference observed between female (M = 3.93, SD = 1.16) and male staff (M = 3.58, SD = 1.30) on Capacity (I-11) (Z = −2.805, p = 0.005 < 0.05). Education level also significantly influences awareness scores, particularly on the status symbol item (I-18) (χ2 = 24.543, p = 0.00 < 0.05). These findings, analyzed through Wilcoxon Rank Sum and Kruskal–Wallis Tests, provide nuanced insights into the demographic factors impacting energy label awareness. Interpretation of the findings on the complex relationship between demographic characteristics and energy label awareness with TPB provided valuable insights. The practical implications of the study highlight the imperative for tailored communication strategies and targeted interventions to raise awareness among different staff groups. By addressing these nuanced factors, the study significantly contributes to the development of effective energy policies, fostering a sustainable and energy-conscious academic community.

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Energy Consumption Characterization in University Campus Microgrid Based on Power Data Analysis

Cited by 2 publications

References 9 publications

Power Consumption Modeling of Airconditioning Units in an Educational Building Using Polynomial and Neural Network Fitting Techniques

Power Consumption Modeling of Airconditioning Units in an Educational Building Using Polynomial and Neural Network Fitting Techniques

Energy Labels and Consumer Attitudes: A Study among University Staff

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