Dr Si Chen
- Research Associate (Autonomous Systems & Connectivity)
Publications
2024
Zhang, M., Millar, M.-A., Chen, S., Ren, Y., Yu, Z. and Yu, J. (2024) Enhancing hourly heat demand prediction through artificial neural networks: a national level case study. Energy and AI, 15, 100315. (doi: 10.1016/j.egyai.2023.100315)
2023
Couraud, B., Andoni, M. , Robu, V., Norbu, S., Chen, S. and Flynn, D. (2023) Responsive FLEXibility: a smart local energy system. Renewable and Sustainable Energy Reviews, 182, 113343. (doi: 10.1016/j.rser.2023.113343)
Ren, Y., Chen, S. and Marco, J. (2023) An adaptive power distribution scheme for hybrid energy storage system to reduce the battery energy throughput in electric vehicles. Transactions of the Institute of Measurement and Control, 45(7), pp. 1367-1381. (doi: 10.1177/01423312221138841)
2022
Andoni, M. , Norbu, S., Couraud, B., Flynn, D. , Chen, S. and Robu, V. (2022) Decentralized Energy White Paper: Adaptive Local Energy Communities. [Research Reports or Papers]
Andoni, M. , Norbu, S., Couraud, B., Flynn, D. , Chen, S. and Robu, V. (2022) Democratising Energy: Placing citizens and communities at the heart of the energy revolution. [Research Reports or Papers]
2021
Chen, S., Ren, Y., Friedrich, D., Yu, Z. and Yu, J. (2021) Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates. Energy and AI, 5, 100093. (doi: 10.1016/j.egyai.2021.100093)
Chen, S., Friedrich, D. and Yu, Z. (2021) Optimal sizing of a grid independent renewable heating system for building decarbonisation. Frontiers in Energy Research, 9, 746268. (doi: 10.3389/fenrg.2021.746268)
2020
Chen, S., Ren, Y., Friedrich, D., Yu, Z. and Yu, J. (2020) Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction. Energy and AI, 2, 100028. (doi: 10.1016/j.egyai.2020.100028)
2019
Chen, S., Friedrich, D., Yu, Z. and Yu, J. (2019) District heating network demand prediction using a physics-based energy model with Bayesian approach for parameter calibration. Energies, 12(18), 3408. (doi: 10.3390/en12183408)
Articles
Zhang, M., Millar, M.-A., Chen, S., Ren, Y., Yu, Z. and Yu, J. (2024) Enhancing hourly heat demand prediction through artificial neural networks: a national level case study. Energy and AI, 15, 100315. (doi: 10.1016/j.egyai.2023.100315)
Couraud, B., Andoni, M. , Robu, V., Norbu, S., Chen, S. and Flynn, D. (2023) Responsive FLEXibility: a smart local energy system. Renewable and Sustainable Energy Reviews, 182, 113343. (doi: 10.1016/j.rser.2023.113343)
Ren, Y., Chen, S. and Marco, J. (2023) An adaptive power distribution scheme for hybrid energy storage system to reduce the battery energy throughput in electric vehicles. Transactions of the Institute of Measurement and Control, 45(7), pp. 1367-1381. (doi: 10.1177/01423312221138841)
Chen, S., Ren, Y., Friedrich, D., Yu, Z. and Yu, J. (2021) Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates. Energy and AI, 5, 100093. (doi: 10.1016/j.egyai.2021.100093)
Chen, S., Friedrich, D. and Yu, Z. (2021) Optimal sizing of a grid independent renewable heating system for building decarbonisation. Frontiers in Energy Research, 9, 746268. (doi: 10.3389/fenrg.2021.746268)
Chen, S., Ren, Y., Friedrich, D., Yu, Z. and Yu, J. (2020) Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction. Energy and AI, 2, 100028. (doi: 10.1016/j.egyai.2020.100028)
Chen, S., Friedrich, D., Yu, Z. and Yu, J. (2019) District heating network demand prediction using a physics-based energy model with Bayesian approach for parameter calibration. Energies, 12(18), 3408. (doi: 10.3390/en12183408)
Research Reports or Papers
Andoni, M. , Norbu, S., Couraud, B., Flynn, D. , Chen, S. and Robu, V. (2022) Decentralized Energy White Paper: Adaptive Local Energy Communities. [Research Reports or Papers]
Andoni, M. , Norbu, S., Couraud, B., Flynn, D. , Chen, S. and Robu, V. (2022) Democratising Energy: Placing citizens and communities at the heart of the energy revolution. [Research Reports or Papers]