| Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction S Chen, Y Ren, D Friedrich, Z Yu, J Yu Energy and AI 2, 100028, 2020 | 53 | 2020 |
| Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates S Chen, Y Ren, D Friedrich, Z Yu, J Yu Energy and AI 5, 100093, 2021 | 23 | 2021 |
| District heating network demand prediction using a physics-based energy model with a Bayesian approach for parameter calibration S Chen, D Friedrich, Z Yu, J Yu Energies 12 (18), 3408, 2019 | 19 | 2019 |
| Responsive FLEXibility: A smart local energy system B Couraud, M Andoni, V Robu, S Norbu, S Chen, D Flynn Renewable and Sustainable Energy Reviews 182, 113343, 2023 | 6 | 2023 |
| Optimal Sizing of a Grid Independent Renewable Heating System for Building Decarbonisation S Chen, D Friedrich, Z Yu Frontiers in Energy Research 9, 746268, 2021 | 3 | 2021 |
| An adaptive power distribution scheme for hybrid energy storage system to reduce the battery energy throughput in electric vehicles Y Ren, S Chen, J Marco Transactions of the Institute of Measurement and Control 45 (7), 1367-1381, 2023 | 2 | 2023 |
| District heat demand prediction using artifi-cial neural network with data of sample building S Chen, Y Ren, D Friedrich, Z Yu, J Yu Proceedings of the international conference on applied energy (ICAE 2019), 12-15, 2019 | 1 | 2019 |
| Decentralized Energy White Paper: Adaptive Local Energy Communities M Andoni, S Norbu, B Couraud, D Flynn, S Chen, V Robu University Of Glasgow, 2022 | | 2022 |
| Democratising Energy: Placing citizens and communities at the heart of the energy revolution M Andoni, S Norbu, B Couraud, D Flynn, S Chen, V Robu University Of Glasgow, 2022 | | 2022 |
| Adaptive Local Energy Communities M Andoni, S Norbu, B Couraud, D Flynn, S Chen, V Robu | | |
Professor Zhibin YuChair of Energy Engineering, University of LiverpoolVerified email at liverpool.ac.uk
