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Accepting PhD Students

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Personal profile

Research interests


Dr Li  graduated from both University of Bath and North China Electric Power University in 2011 with BEng in electrical power engineering.

He received his PhD in 2014 from Bath, with thesis entitled “Load profiling on time-frequency domain: from big data to smart data”.

Between 2014 and 2015, Ran worked as research associate (RA) in an EPSRC UK-China smart grid project. He took up a lectureship in September 2015.

Research interests

Dr Li has developed a set of big data analytic techniques based on statistics and spectral analysis in order to address challenges brought by smart grid and smart meters. He has extensive experience in working with UK power industry and regulator in low carbon distribution network development and smart metering reform.

Willing to supervise PhD

Interested in supervising students. PhD studentship available. Interested candidates should contact Dr Li directly.

Fingerprint Dive into the research topics where Ran Li is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2015 2020

Customer-Led Distribution System (From DNO to DSO)

Li, F., Gu, C., Hernando Gil, I., Li, R., Ma, K. & Pei, X.


Project: UK industry


Li, R., Le Blond, S. & Li, F.

Engineering and Physical Sciences Research Council


Project: Research council

Energy security
Toxic materials

Research Output 2012 2019

50 Downloads (Pure)

A Statistical Approach to Estimate Imbalance-Induced Energy Losses for Data-Scarce Low Voltage Networks

Fang, L., Ma, K., Li, R., Wang, Z. & Shi, H., 1 Jul 2019, In : IEEE Transactions on Power Systems. 34, 4, p. 2825-2835 11 p., 8606229.

Research output: Contribution to journalArticle

Open Access
2 Citations (Scopus)
67 Downloads (Pure)

Big Data Analytics for Flexible Energy Sharing: Accelerating a Low-Carbon Future

Li, F., Li, R., Zhang, Z., Dale, M., Tolley, D. & Ahokangas, P., 1 May 2018, In : IEEE Power and Energy Magazine. 16, 3, p. 35-42 8 p.

Research output: Contribution to journalArticle

Open Access
57 Citations (Scopus)
922 Downloads (Pure)

Deep Learning for Household Load Forecasting – A Novel Pooling Deep RNN

Shi, H., Xu, M. & Li, R., 30 Sep 2018, In : IEEE Transactions on Smart Grids. 9, 5, p. 5271-5280 10 p.

Research output: Contribution to journalArticle

Open Access
Uncertainty analysis
Recurrent neural networks
Spectrum analysis
Learning algorithms
Learning systems

Electric Vehicle Load Disaggregation Based on Limited Activation Matching Pursuits

Li, R., Evans, A., Li, F. & Wang, S., 2018.

Research output: Contribution to conferencePaper

Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level

Yan, X., Ma, Q., Wyman-Pain, H., Gu, C., Li, R. & Li, F., 16 Jan 2018, Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 . IEEE, p. 1-5 5 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Energy storage
Cost reduction


3D Face Recognition Using Multicomponent Feature Extraction from the Nasal Region and its Environs

Author: Gao, J., 23 Nov 2016

Supervisor: Evans, A. (Supervisor) & Li, R. (Supervisor)

Student thesis: Doctoral ThesisPhD


Load Profiling on Time and Spectral Domain: From Big Data to Smart Data

Author: Li, R., 19 Jan 2015

Supervisor: Li, F. (Supervisor)

Student thesis: Doctoral ThesisPhD


Smart Meter Data Analytics

Author: Xu, M., 29 May 2019

Supervisor: Li, R. (Supervisor), Evans, A. (Supervisor) & Li, F. (Supervisor)

Student thesis: Doctoral ThesisPhD