A Novel Sensor Platform Matching the Improved Version of IPMVP Option C for Measuring Energy Savings

Tseng, Yen-Chieh; Lee, Dasheng; Lin, Cheng-Fang; Chang, Ching‐Yuan

doi:10.3390/s130506811

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…Figure 4 shows the data points by envelope line to investigate energy-saving potentials. Panel (a) in Figure 4 shows that the EUI values of chain stores have a certain range regarding different series, similar to the conclusion of the authors' previous study [ 16 ], which requires checking if a store belongs to a chain-store series. Otherwise, stores have distributed EUI values, shown on the left, and must be investigated using other information collected from websites.…”

Section: Constructing An 8d Data Array As a Sensor Core To Investigatsupporting

confidence: 80%

Investigating Energy-Saving Potentials in the Cloud

Lee

2014

Sensors

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Collecting webpage messages can serve as a sensor for investigating the energy-saving potential of buildings. Focusing on stores, a cloud sensor system is developed to collect data and determine their energy-saving potential. The owner of a store under investigation must register online, report the store address, area, and the customer ID number on the electric meter. The cloud sensor system automatically surveys the energy usage records by connecting to the power company website and calculating the energy use index (EUI) of the store. Other data includes the chain store check, company capital, location price, and the influence of weather conditions on the store; even the exposure frequency of store under investigation may impact the energy usage collected online. After collecting data from numerous stores, a multi-dimensional data array is constructed to determine energy-saving potential by identifying stores with similarity conditions. Similarity conditions refer to analyzed results that indicate that two stores have similar capital, business scale, weather conditions, and exposure frequency on web. Calculating the EUI difference or pure technical efficiency of stores, the energy-saving potential is determined. In this study, a real case study is performed. An 8-dimensional (8D) data array is constructed by surveying web data related to 67 stores. Then, this study investigated the savings potential of the 33 stores, using a site visit, and employed the cloud sensor system to determine the saving potential. The case study results show good agreement between the data obtained by the site visit and the cloud investigation, with errors within 4.17%. Among 33 the samples, eight stores have low saving potentials of less than 5%. The developed sensor on the cloud successfully identifies them as having low saving potential and avoids wasting money on the site visit.

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Section: Constructing An 8d Data Array As a Sensor Core To Investigatsupporting

confidence: 80%

Investigating Energy-Saving Potentials in the Cloud

Lee

2014

Sensors

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“…After these tests, the International Performance Measurement and Verification Protocol (IPMVP) is recommended for the M&V stage. The IPMVP was developed in 1994‐1995 by a coalition of international organizations led by the United States Department of Energy 121 . This protocol lists four optional approaches for determining savings.…”

Section: Aiif Developmentmentioning

confidence: 99%

Artificial intelligence implementation framework development for building energy saving

et al. 2020

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In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five-category AI control tools to execute three-level building energy conservation; the three levels consist of equipment-level control, facility-level control, and whole building energy saving. Energy-saving effects were tested in a real building. The complex three-floor building primarily with a total area of 9072 m 2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed. K E Y W O R D S artificial intelligence (AI), artificial intelligence implementation framework (AIif), building energy saving, equipment-level control, facility-level control List of Symbols, Abbreviations, and Notation: ω i , weighting coefficients; GS T , the sum of the global similarities between the selected m cases; MV i , the mean difference of the variable i; P j , proportion of the prediction; V 1 , variation of control parameter y; y ic , the neural outputs of variable i for the control; y ip , the neural outputs of variable i for past cases; AI

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“…The calculated energy saving results may include the information of energy saving amount and saved energy usage. Some details related to energy savings comparison between the sensor platform and IPMVP can be referred to our previous work [15].…”

Section: A New Business Model Created By the Mandv Systemmentioning

confidence: 99%

The Development of Cloud Energy Management

et al. 2015

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Abstract:The energy management service (EMS) has been utilized for saving energy since 1982 by managing the energy usage of site or facilities through the microprocessor, computer, Ethernet, internet, and wireless sensor network. The development and represented function groups of EMS are illustrated in the supplementary file of this paper. Along with this tendency, a cloud EMS, named the intelligent energy management network (iEN), was launched by Chunghwa Telecom in 2011 and tested during a pilot run from 2012 to 2013. The cloud EMS integrated three service modes together, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). This cloud EMS could reduce the facility cost and enable a continuously improved service for energy conservation. From the literature review, 32 selected EMS cases of whole site and single facility were chosen for calculating the energy savings and payback rate. According to the literature, the average energy savings by applying EMS are 11.6% and 21.4% for the whole site and single facility, respectively. The iEN was applied on 55 demo sites with the similar scale, the same kind of machines and approaching conditions. The testing sites include a factory, a complex building, and a residual building, 12 lighting systems and 8 air conditioning systems. According to the testing results, the average energy savings by applying iEN are 10% and 23.5% for the whole site and single facility, respectively. OPEN ACCESSEnergies 2015, 8 4358Comparing with the reported EMS cases, it was found that the energy savings by adopting the cloud EMS were only 70%-80% compared with those using the traditional EMS. Although the cloud EMS presented less energy savings, it revolutionized the traditional EMS by its innovative business model. Compared with the averaged 1.7 years payback period of the traditional EMS, more than 70% of the cloud EMS cases could pay back immediately for the service fees and without the equipment investment.Keywords: cloud energy management service (cloud EMS); intelligent energy management network (iEN); software as a service (SaaS); immediate payback

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A Novel Sensor Platform Matching the Improved Version of IPMVP Option C for Measuring Energy Savings

Cited by 10 publications

References 10 publications

Investigating Energy-Saving Potentials in the Cloud

Investigating Energy-Saving Potentials in the Cloud

Artificial intelligence implementation framework development for building energy saving

The Development of Cloud Energy Management

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