ICT & Data Analytics

ICT plays an essential role in smart grid.  It brings huge amount of measured values to data centers.  The data are produced from millions of meters/sensors, installed at many locations.  Then the data centers convert them to useful information with big data analysis.  The collected data may include produced or consumed energy/power,  temperatures, wind speed, cloud images, etc.  And the information can be used to plan electricity production, transmission, O&M, asset management, etc.

SGRU aims to find efficient techniques for  secure data transmission.  It is also interested in algorithms for renewable energy/load forecast.  In this area, SGRU’s researchers have collaborations with their counterparts from Japan, the USA, Germany and China.   One of SGRU’s missions is to establish a center for RE & load forecast.

Research Team

  1. Wanchaleam Pora
  2. Jitkomut Songsiri
  3. Naebboon Hoonchareon
  4. Manisa Pipattanasomporn
  5. David Banjerdpongchai

Equipment and Testbed

Recent publication

  • Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds, Alrashidi, M., Rahman, S., Pipattanasomporn, M., Renewable Energy 2020.
  • Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks, Chitalia, G., Pipattanasomporn, M., Garg, V., Rahman, S., Applied Energy 2020.
  • CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets, Pipattanasomporn, M., Chitalia, G., Songsiri, J., Aswakul, C., Pora, W., Suwankawin, S., Audomvongseree, K., Hoonchareon, N., Scientific Data 2020.
  • A transactive grid with microgrids using blockchain for the energy internet, 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, Dimobi, I, Pipattanasomporn, M., ISGT 2020.
  • Realizing the potential of blockchain technology in smart grid applications, Kuzlu, M., Sarp, S., Pipattanasomporn, M., Cali, U., ISGT 2020.
  • Occupancy Forecasting using LSTM Neural Network and Transfer Learning, Leeraksakiat, P., Pora, W., ECTI-CON 2020.
  • New Rectification Technique for Cloud Base Height Estimation Base on Stereo Cameras, Prasertseree, C., Pora, W., ECTI-CON 2020.
  • Power Baseline Modeling for Split-Type Air Conditioner in Building Energy Management Systems Using Deep Learning, Chumnanvanichkul, P., Chirapongsananurak P., Hoonchareon N., APCCAS 2019.
  • Three-level Classification of Air Conditioning Energy Consumption for Building Energy Management System Using Data Mining Techniques, Chumnanvanichkul, P., Chirapongsananurak P., Hoonchareon N., IEEE PES GTD Asia 2019.
  • Missing-data imputation for solar irradiance forecasting in Thailand, Layanun, V., Suksamosorn, S., Songsiri, J., SICE 2017.
  • A study on the gustafson-kessel clustering algorithm in power system fault identification, Abdullah, A., Banmongkol, C., Hoonchareon, N., Hidaka, K., Journal of Electrical Engineering and Technology 2017.
  • Implementation of adaptive neuro-fuzzy inference system in fault location estimation, Abdullah, A., Banmongkol, C., Hoonchareon, N., Hidaka, K., Lecture Notes in Electrical Engineering 2017.
  • Classification of transmission line faults with waveform characterization, Hongdilokkul, R., Banmongkol, C., ECTI-CON 2016.
  • A low-cost Wi-Fi smart plug with on-off and Energy Metering functions, Thongkhao, Y., Pora, W., ECTI-CON 2016.
  • A development of IEC61850 monitoring system for distribution transformer, Srisodsai, S., Pora, W., ECTI-CON 2016.
  • Parameter extraction for a solar cell model with three measured points only, Tong, N.T., Pora, W., ICEIC 2016.
  • A parameter extraction technique exploiting intrinsic properties of solar cells, Tong, N.T., Pora, W., Applied Energy 2016.