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Optimising multi-factor assistance in a deep learning-based electricity forecasting model with climate resilience: an Australian case study., , and . ISGT EUROPE, page 1-5. IEEE, (2023)Utilising Amazon web services to provide an on demand urgent computing facility for climateprediction.net., , , and . eScience, page 407-413. IEEE Computer Society, (2016)Feature extraction to characterise and cluster the energy demand of UK retail premises., , and . EnviroInfo/ICT4S (2), page 223-224. University of Copenhagen, (2015)A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas., , and . CoRR, (2023)Demand Response Analytical Framework for Assessing Programme Applicability across Countries., and . ISGT Asia, page 1-5. IEEE, (2023)The International Forest Risk Model (INFORM): A Method for Assessing Supply Chain Deforestation Risk with Imperfect Data., , and . eScience, page 11-17. IEEE, (2019)Cloud Computing for Climate Modelling: Evaluation, Challenges and Benefits., , , , , and . Comput., 9 (2): 52 (2020)A Dirichlet Process Mixture Model for Clustering of Household Electricity Load Profiles., , and . MLDM Posters, page 1-6. ibai Publishing, (2013)Recent developments towards novel high performance computing and communications solutions for smart distribution network operation., , , , and . ISGT Europe, page 1-8. IEEE, (2011)Investigation of Data Communication Networks to Enable Demand Response and Dynamic Tariffs., , , , and . GreenCom/iThings/CPScom, page 320-323. IEEE, (2013)