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ORCIDHussain Kazmi
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2020 – today
- 2023
[c7]Mohamad Khalil
, A. Stephen McGough, Hussain Kazmi, Sara Walker:
The Forecastability of Underlying Building Electricity Demand from Time Series Data. IEEE Big Data 2023: 3785-3793- [c6]Attila Balint, Haroon Raja, Johan Driesen, Hussain Kazmi:
Using domain-augmented federated learning to model thermostatically controlled loads. ISGT EUROPE 2023: 1 - [c5]Fuyang Jiang, Johan Driesen, Hussain Kazmi:
On the causality of data-driven building thermal models. BuildSys@SenSys 2023: 454-457 - [i3]Hussain Kazmi, Pierre Pinson:
On the contribution of pre-trained models to accuracy and utility in modeling distributed energy resources. CoRR abs/2302.11679 (2023) - [i2]Mohamad Khalil, A. Stephen McGough, Hussain Kazmi, Sara Walker:
The Forecastability of Underlying Building Electricity Demand from Time Series Data. CoRR abs/2311.18078 (2023) - 2022
- [i1]Maria Margarida Mascarenhas, Hussain Kazmi:
Is your forecaster smarter than an energy engineer: a deep dive into electricity price forecasting. CoRR abs/2209.13411 (2022) - 2021
- [c4]Khaoula Tidriri, Ahmad Braydi, Hussain Kazmi:
Data-driven Decision-Making Methodology for Prognostic and Health Management of Wind Turbines. ANZCC 2021: 104-109 - [c3]Hussain Kazmi:
Teaching machine learning through end-to-end decision making. Teaching ML 2021: 10-14
2010 – 2019
- 2017
- [c2]Adhra Ali, Hussain Kazmi:
Minimizing Grid Interaction of Solar Generation and DHW Loads in nZEBs Using Model-Free Reinforcement Learning. DARE@PKDD/ECML 2017: 47-58 - 2016
- [c1]Hussain Kazmi, Simona D'Oca:
Demonstrating model-based reinforcement learning for energy efficiency and demand response using hot water vessels in net-zero energy buildings. ISGT Europe 2016: 1-6
Coauthor Index
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