Online Methodology for Separating the Power Consumption of Lighting Sockets and Air-Conditioning in Public Buildings Based on an Outdoor Temperature Partition Model and Historical Energy Consumption Data

Zhao, Tianyi; Zhang, Chengyu; Ujeed, Terigele; Ma, Liangdong

doi:10.3390/app11031031

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Buildings, like vehicles, are driven by their occupants. Several international studies have documented that energy use in buildings is closely linked to occupant presence, movement and interaction with building energy devices and systems [22][23][24]. The assumed occupant behaviors, setpoints and indoor conditions as well as other simplified assumptions in building energy calculations will result in significant deviations between the estimated and actual energy use.…”

Section: Occupant Behaviormentioning

confidence: 99%

Impacts on Indoor Thermal Comfort and Heating Energy Use in Hellenic Dwellings from Occupant Behavioral Reactions

Dascalaki¹,

Balaras²

2021

Applied Sciences

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In an effort to reduce the operational cost of their dwellings, occupants may even have to sacrifice their indoor thermal comfort conditions. Following the economic recession in Greece over recent years, homeowners have been forced to adapt their practices by shortening heating hours, lowering the indoor thermostat settings, isolating spaces that are not heated or even turning off their central heating system and using alternative local heating systems. This paper presents the results from over 100 occupant surveys using questionnaires and walk-through energy audits in Hellenic households that documented how occupants operated the heating systems in their dwellings and the resulting indoor thermal comfort conditions and actual energy use. The results indicate that the perceived winter thermal comfort conditions were satisfactory in only half of the dwellings, since the actual operating space heating periods averaged only 5 h (compared with the assumed 18 h in standard conditions), while less than half heated their entire dwellings and only a fifth maintained an indoor setpoint temperature of 20 °C, corresponding to standard comfort conditions. Mainstream energy conservation measures include system maintenance, switching to more efficient systems, reducing heat losses and installing controls. This information is then used to derive empirical adaptation factors for bridging the gap between the calculated and actual energy use, making more realistic estimates of the expected energy savings following building renovations, setting prudent targets for energy efficiency and developing effective plans toward a decarbonized building stock.

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Section: Occupant Behaviormentioning

confidence: 99%

Impacts on Indoor Thermal Comfort and Heating Energy Use in Hellenic Dwellings from Occupant Behavioral Reactions

Dascalaki¹,

Balaras²

2021

Applied Sciences

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“…In addition to this study, methods of clustering and grading mechanisms were also applied in other studies. 23,24 Moreover, clustering and other similar classification methods were used in various types of research, such as abnormal energy data recognition, 25 equipment operating status recognition, 26 and building energy demand or consumption prediction. 27 Although relevant research made some progress, there were still some challenges regarding clustering algorithms that needed to be solved.…”

Section: Introductionmentioning

confidence: 99%

Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings

Zhao

Zhang

Ujeed

et al. 2022

Building Services Engineering Research and Technology

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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. Practical application This paper proposed a matrix model method based on the conventional K-nearest-neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method was applied to various buildings online, which coupled the process of filtering historical data and flexible selectivity of models when used on different buildings. This method was used for assessing energy-saving potential, energy-saving retrofit priorities, and power consumption forecasting, which will benefit researchers and engineers.

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“…With a smart meter, every appliance employed in homes and buildings may be monitored with a schedule, remotely governed, and watched over by smart network technology [15]. Therefore, the monitoring smart system will be necessary for the smart distribution network, and monitoring electricity consumption may help lower customer demand for power by promoting a culture of increased electricity consumption while also outlining the associated costs [16]. The current article introduced a new monitoring system that measures the three-phase AC currents in any building utilizing two microcontrollers (Arduino and ESP32) and three current sensors.…”

mentioning

confidence: 99%

Development of Wireless Smart Current Sensor for Power Monitoring System

Shafeeq,

Mahammad,

Salih

2023

IJEER

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The traditional distribution network must be replaced by a smart grid, durable, and dependable, due to the rising demand for power by the customer. The smart grid will need a smart monitoring system based on the smart current sensor at all buildings to determine the power consumption with its costs. This paper proposed a wireless monitoring system for measuring the three-phase currents in the building by using three HW-666 current sensors with Arduino and ESP32 microcontrollers to transmit the data according to the Internet of Things (IoT) technique to the Telegram app on the mobile phone. The power consumption is determined by assuming the constant voltages and power factor at the building that is tested with this monitoring system. The results are scheduled on the personal computer (PC) by using the serial monitor of the Arduino software and also by receiving the message every 20 minutes on the telegram app furthermore, real-time data can be obtained immediately upon receiving transmitted commands from the Telegram application. In addition, this monitoring system is designed to cut off power to the building and sound an alarm if abnormal currents are detected. The proposed monitoring system is given the same results when a comparison with the ammeter with errors not exceeding 10%.

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Online Methodology for Separating the Power Consumption of Lighting Sockets and Air-Conditioning in Public Buildings Based on an Outdoor Temperature Partition Model and Historical Energy Consumption Data

Cited by 5 publications

References 24 publications

Impacts on Indoor Thermal Comfort and Heating Energy Use in Hellenic Dwellings from Occupant Behavioral Reactions

Impacts on Indoor Thermal Comfort and Heating Energy Use in Hellenic Dwellings from Occupant Behavioral Reactions

Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings

Development of Wireless Smart Current Sensor for Power Monitoring System

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