Electricity Management Policy Applying Data Science and Machine Learning Techniques to Improve Electricity Costs

This paper studies the actual electricity case of a national university in northern Taiwan, pointing out that many schools will face certain asymmetrical information and practical problems in the development of power systems, such as energy-savings and carbon-reduction policies, collecting electrici...

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Bibliographic Details
Published in:Symmetry (Basel) 2022-10, Vol.14 (10), p.2104
Main Authors: Lee, Chun-Yao, Huang, Kuan-Yu, Chen, Chun-Chi, Zhuo, Guang-Lin, Tuegeh, Maickel
Format: Article
Language:English
Subjects:
Accuracy
Automatic meter reading
Big Data
big data platform
Blackouts
Consumers
Customers
Data analysis
Data processing
Data science
Electric power distribution
Electricity
electricity costs
Electricity distribution
Energy
Energy costs
energy-saving policy
Fees & charges
Forecasting
Machine learning
Mathematical programming
Methods
Neural networks
Outages
Power supply
Smart grid technology
Time series
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Summary:This paper studies the actual electricity case of a national university in northern Taiwan, pointing out that many schools will face certain asymmetrical information and practical problems in the development of power systems, such as energy-savings and carbon-reduction policies, collecting electricity fees in each division, reducing the loss of power outages, expanding the power system capacity, and maintaining power distribution equipment. These problems are closely related to electricity costs, which include general electricity fees, unexpected losses caused by power outages, purchases of replacement power equipment, and maintenance fees of distribution equipment. This paper proposes corresponding improvement plans for each of the problems in the above-mentioned actual case studies and assists school power managers in using symmetrical information to formulate the best strategies to improve electricity costs.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14102104