Short-term load forecasting using artificial neural networks and social media data (2021)
Conference Proceeding
Boyd, A., Sun, H., Black, M., & Jesson, S. (2021). Short-term load forecasting using artificial neural networks and social media data. . https://doi.org/10.1049/icp.2021.1552

Evolving practices around energy generation, storage and trading within the UK have made it more necessary than ever to provide accurate means of forecasting electricity demand. This paper considers deep neural networks with convolutional and recurre... Read More about Short-term load forecasting using artificial neural networks and social media data.

Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems (2021)
Conference Proceeding
Hua, W., Jing, R., Zhou, Y., Zhang, X., Jiang, J., & Sun, H. (2022). Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems. . https://doi.org/10.46855/energy-proceedings-9190

Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems d... Read More about Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems.

Appliance Classification using BiLSTM Neural Networks and Feature Extraction (2021)
Conference Proceeding
Correa-Delval, M., Sun, H., Matthews, P., & Jiang, J. (2021). Appliance Classification using BiLSTM Neural Networks and Feature Extraction. . https://doi.org/10.1109/isgteurope52324.2021.9640061

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.

Using machine learning for the classification of the remaining useful cycles in Lithium-ion batteries (2021)
Conference Proceeding
Coutts, H., & Wang, Q. (2021). Using machine learning for the classification of the remaining useful cycles in Lithium-ion batteries. . https://doi.org/10.1115/detc2021-69647

In order to keep up with the increasing focus on renewable energy, the demand for new battery technology and peripherals has likewise increased greatly. Given the relatively slow rate of change of new battery chemistry and technology, it is the perip... Read More about Using machine learning for the classification of the remaining useful cycles in Lithium-ion batteries.

Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres (2021)
Conference Proceeding
Mackie, M., Sun, H., & Jiang, J. (2021). Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres.

This paper proposes a method of migrating workload among geo-distributed data centres that are equipped with on-site renewable energy sources (RES), such as solar and wind energy, to decarbonise data centres. It aims to optimise the performance of su... Read More about Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres.

Scaling Down Power (2021)
Conference Proceeding
Concetti, C. (2021). Scaling Down Power.

Distributed Generation systems (DG) are small, often renewable, and localised technologies that feed electricity directly into distribution networks, including rooftop solar panels, small wind turbines, and micro hydro-electric screws (Parag & Sovaco... Read More about Scaling Down Power.

A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming (2021)
Conference Proceeding
You, M., Zheng, G., & Sun, H. (2021). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. . https://doi.org/10.1109/icc42927.2021.9500736

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in the multiuser multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major chal... Read More about A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming.

Price Forecast Methodologies Comparison for Microgrid Control with Multi-Agent Systems (2021)
Conference Proceeding
Cruz Victorio, M., Kazemtabrizi, B., & Shahbazi, M. (2021). Price Forecast Methodologies Comparison for Microgrid Control with Multi-Agent Systems. . https://doi.org/10.1109/powertech46648.2021.9494970

Multi-Agent systems offer a way to control distributed generation in microgrids, reliability and cost minimisation capabilities can be improved by price forecast methodologies that can be deployed without the need of external control signals. This pa... Read More about Price Forecast Methodologies Comparison for Microgrid Control with Multi-Agent Systems.

Trust-based Model for Securing Vehicular Networks Against RSU Attacks (2021)
Conference Proceeding
Alnasser, A., & Sun, H. (2021). Trust-based Model for Securing Vehicular Networks Against RSU Attacks. . https://doi.org/10.1109/infocomwkshps51825.2021.9484458

Intelligent Transportation System (ITS) is one of the Internet of Things (IoT) systems that can achieve reliable transportation by providing communications between vehicles and infrastructure units. The interaction between them is called Vehicle-to-E... Read More about Trust-based Model for Securing Vehicular Networks Against RSU Attacks.