| A critical review of comparative global historical energy consumption and future demand: The story told so far T Ahmad, D Zhang Energy Reports 6, 1973-1991, 2020 | 388 | 2020 |
| A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review T Ahmad, H Chen, Y Guo, J Wang Energy and Buildings 165, 301-320, 2018 | 246 | 2018 |
| A review on renewable energy and electricity requirement forecasting models for smart grid and buildings T Ahmad, H Zhang, B Yan Sustainable Cities and Society 55, 102052, 2020 | 245 | 2020 |
| Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities T Ahmad, D Zhang, C Huang, H Zhang, N Dai, Y Song, H Chen Journal of Cleaner Production 289, 125834, 2021 | 221 | 2021 |
| Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving Y Guo, Z Tan, H Chen, G Li, J Wang, R Huang, J Liu, T Ahmad Applied Energy 225, 732-745, 2018 | 133 | 2018 |
| Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches T Ahmad, H Chen Energy and Buildings 166, 460-476, 2018 | 122 | 2018 |
| Review of various modeling techniques for the detection of electricity theft in smart grid environment T Ahmad, H Chen, J Wang, Y Guo Renewable and Sustainable Energy Reviews 82, 2916-2933, 2018 | 109 | 2018 |
| Non-technical loss analysis and prevention using smart meters T Ahmad Renewable and Sustainable Energy Reviews 72, 573-589, 2017 | 108 | 2017 |
| A review on machine learning forecasting growth trends and their real-time applications in different energy systems T Ahmad, H Chen Sustainable Cities and Society 54, 102010, 2020 | 106 | 2020 |
| Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems T Ahmad, H Chen Sustainable Cities and Society 45, 460-473, 2019 | 106 | 2019 |
| Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment T Ahmad, H Chen, R Huang, G Yabin, J Wang, J Shair, HMA Akram, ... Energy 158, 17-32, 2018 | 91 | 2018 |
| Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment T Ahmad, H Chen Energy 160, 1008-1020, 2018 | 79 | 2018 |
| Utility companies strategy for short-term energy demand forecasting using machine learning based models T Ahmad, H Chen Sustainable Cities and Society 39, 401-417, 2018 | 72 | 2018 |
| Using the internet of things in smart energy systems and networks T Ahmad, D Zhang Sustainable Cities and Society 68, 102783, 2021 | 68 | 2021 |
| Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of … T Ahmad, R Madonski, D Zhang, C Huang, A Mujeeb Renewable and Sustainable Energy Reviews 160, 112128, 2022 | 62 | 2022 |
| Deep learning for multi-scale smart energy forecasting T Ahmad, H Chen Energy 175, 98-112, 2019 | 57 | 2019 |
| An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems Y Guo, J Wang, H Chen, G Li, R Huang, Y Yuan, T Ahmad, S Sun Applied Thermal Engineering 149, 1223-1235, 2019 | 56 | 2019 |
| Demand response for industrial micro-grid considering photovoltaic power uncertainty and battery operational cost C Huang, H Zhang, Y Song, L Wang, T Ahmad, X Luo IEEE Transactions on Smart Grid 12 (4), 3043-3055, 2021 | 51 | 2021 |
| A novel energy demand prediction strategy for residential buildings based on ensemble learning Y Huang, Y Yuan, H Chen, J Wang, Y Guo, T Ahmad Energy Procedia 158, 3411-3416, 2019 | 45 | 2019 |
| Energetics systems and artificial intelligence: applications of industry 4.0 T Ahmad, H Zhu, D Zhang, R Tariq, A Bassam, F Ullah, AS AlGhamdi, ... Energy Reports 8, 334-361, 2022 | 43 | 2022 |
