A Study on the Business Data Evaluation Method of the Power Grid Value-Added Service

Jun, Lu; Li, Suxiu; Zhang, Xinsheng

doi:10.1109/icpee54380.2021.9662594

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Wang (2010) [3] subdivides power customers from the perspectives of current economic contribution, power consumption management level, customer credit status, customer social influence and customer development potential, and uses fuzzy analytic hierarchy process (AHP) to make comprehensive evaluation of power customer value. Lu & Liu (2016) [4] established the power customer segmentation index system from three dimensions of current value, potential value and credit value, and the index content included six aspects such as power consumption characteristics, development potential and economic credit, and developed corresponding value-added services, and carried out cluster analysis of power customers with K-means algorithm. Qiu et al (2018) [5] constructed an electric power customer index system from three dimensions: current value, potential value, and regional value of electric power customer, including 10 three-level indexes such as service cost, customer credit, security level, customer social influence, customer loyalty, and constructed an ideal fuzzy matter-element evaluation model to evaluate the comprehensive value of electric power customer.…”

Section: Introductionmentioning

confidence: 99%

Value analysis of power customers based on combined weight-TOPSIS

Chen

WANG

Wang

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

View full text Add to dashboard Cite

High-quality customers play an important role in the sustained development of power enterprises. Power customer value analysis is the basis for power enterprises to implement differentiated services for customers of different values and maximize benefits. Based on the analysis of power customer value, this paper constructs a customer value evaluation index system including current value and potential value, and determines the combined weight of evaluation index by using Analytic Hierarchy Process (AHP) and entropy weight method, and then combines with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to construct an power customer value evaluation model based on combined weight-TOPSIS. Lastly, 15 customers of a prefecture-level city power supply enterprise are selected for specific analysis to verify the effectiveness and practicability of the customer value evaluation model.

show abstract

Section: Introductionmentioning

confidence: 99%

Value analysis of power customers based on combined weight-TOPSIS

Chen

WANG

Wang

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

View full text Add to dashboard Cite

show abstract

Towards a taxonomy for business capabilities determining data value

Hafner,

Mira da Silva

2023

Knowl Inf Syst

View full text Add to dashboard Cite

Data and its valuation have gained vital significance in academia and enterprises, coinciding with diverse data valuation approaches encompassing various layers, dimensions, and characteristics. This paper assesses data value determination through a business capability lens based on the TOGAF standard. The paper encompasses (a) constructing a Data Valuation Business Capability (DVBC) taxonomy and (b) validating the taxonomy using two existing data valuation concepts from academia. The methodology involves information systems taxonomy development techniques backed by a previously conducted systematic literature review of 64 articles. The resultant taxonomy comprises four business capability layers, nine dimensions, and 36 characteristics. These layers and dimensions offer business, technology, and organizational perspectives, reflecting the interdisciplinary nature of data valuation alongside an enterprise architecture. Characteristics within these layers and dimensions are either exclusive or non-exclusive based on their contents. The compiled findings meet both objective and subjective quality criteria. The implications of the DVBC are multifaceted, influencing scholars and professionals alike. Scholars gain a cohesive tool enhancing transparency in the extensively debated data value domain, fostering linkages among information systems, enterprise architecture management, and data management. This empowers the progress in developing comprehensive data valuation concepts. Additionally, professionals may employ the DVBC taxonomy as a lighthouse and guiding tool, fostering internal dialog on data valuation. This entails elevating data valuation to a pivotal business capability, necessitating collaborative, regular assessment, and enhancement involving business and technological stakeholders. By adopting this taxonomy, the challenge of consistently determining data value can be effectively addressed in both academia and enterprises.

show abstract

A Study on the Business Data Evaluation Method of the Power Grid Value-Added Service

Cited by 2 publications

References 10 publications

Value analysis of power customers based on combined weight-TOPSIS

Value analysis of power customers based on combined weight-TOPSIS

Towards a taxonomy for business capabilities determining data value

Contact Info

Product

Resources

About