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Martha Correa Delval's Outputs (3)

Decarbonising Heating with Power-Hydrogen Optimisation (2024)
Presentation / Conference Contribution
Gonzalez-Osuna, E., Correa-Delval, M., & Sun, H. (2024, November). Decarbonising Heating with Power-Hydrogen Optimisation. Presented at 9th International Conference on Renewable Energy and Conservation ICREC 2024, Rome, Italy

This paper presents an analysis of an integrated power-hydrogen system for an energy community, incorporating renewable energy sources as solar panels, battery storage, and grid interaction. This study focuses on optimising energy consumption and min... Read More about Decarbonising Heating with Power-Hydrogen Optimisation.

Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems (2022)
Presentation / Conference Contribution
Correa-Delval, M., Sun, H., Matthews, P. C., & Chiu, W.-Y. (2022, September). Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems. Presented at 2022 5th International Conference on Renewable Energy and Power Engineering (REPE 2021), Beijing, China

In recent years, the importance of reducing carbon dioxide (CO2) emissions has increased. With the use of technologies such as artificial intelligence, we can improve the way households manage their energy use to decrease cost and carbon emissions. I... Read More about Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems.

Appliance Classification using BiLSTM Neural Networks and Feature Extraction (2021)
Presentation / Conference Contribution
Correa-Delval, M., Sun, H., Matthews, P., & Jiang, J. (2021, October). Appliance Classification using BiLSTM Neural Networks and Feature Extraction. Presented at IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Espoo, Finland

One significant challenge in Non-Intrusive Load Monitoring (NILM) is to identify and classify active appliances used in a building. This research focuses on the classifying process, exploring different approaches for the feature extraction of the app... Read More about Appliance Classification using BiLSTM Neural Networks and Feature Extraction.