Outputs (160)

Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G (2023)
Presentation / Conference Contribution
Thomas, H., Sun, H., & Kazemtabrizi, B. (2023, March). Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G. Presented at ISGT ME 2023, Abu Dhabi, UAE

The uptake of electric vehicles and distributed energy generation is adding significant new demand to distribution networks, however it is unknown whether this can be accommodated by existing infrastructure. This paper first presents an Optimisation... Read More about Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G.

Electric Vehicle Battery Pack Design for Mitigating Thermal Runaway Propagation (2022)
Presentation / Conference Contribution
Copsey, E., Sun, H., & Jiang, J. (2022, September). Electric Vehicle Battery Pack Design for Mitigating Thermal Runaway Propagation. Presented at 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London

The production of electric vehicle battery packs with ever-increasing energy densities has accelerated the electrification of the world’s automotive industry. With increased attention on the electric vehicle markets, it is vital to increase the safet... Read More about Electric Vehicle Battery Pack Design for Mitigating Thermal Runaway Propagation.

Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality (2022)
Presentation / Conference Contribution
Piper, W., Sun, H., & Jiang, J. (2022, September). Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality. Presented at 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London

Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital tw... Read More about Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality.

A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization (2022)
Presentation / Conference Contribution
Deng, H., Jiang, J., Qian, H., & Sun, H. (2022, October). A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization. Presented at 6th International Conference on Smart Grid and Smart Cities (ICSGSC 2022), Chengdu, China

Microgrid is playing an increasingly important role in making the utility grid more intelligent and efficient, since it can make better use of the renewable energy resources to simultaneously relieve the grid supply pressure and reduce carbon emissio... Read More about A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization.

Demand side management considering household appliances and EV (2022)
Presentation / Conference Contribution
Dong, Z., Jiang, J., Qian, H., & Sun, H. (2022, October). Demand side management considering household appliances and EV. Presented at 6th International Conference on Smart Grid and Smart Cities (ICSGSC 2022), Chengdu, China

Combination of the information technology and the power engineering is the feature of next-generation grid. Depending on bidirectional communications, demand side management (DSM) aims at optimizing the electricity usage pattern of customers to impro... Read More about Demand side management considering household appliances and EV.

Protecting privacy in microgrids using federated learning and deep reinforcement learning (2022)
Presentation / Conference Contribution
Chen, W., Sun, H., Jiang, J., You, M., & Piper, W. (2022, November). Protecting privacy in microgrids using federated learning and deep reinforcement learning. Presented at 12th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2022), Hong Kong, China

This paper aims to improve the energy management efficiency of home microgrids while preserving privacy. The proposed microgrid model includes energy storage systems, PV panels, loads, and the connection to the main grid. A federated multi-objective... Read More about Protecting privacy in microgrids using federated learning and deep reinforcement learning.

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.

Evaluating Gaussian Grasp Maps for Generative Grasping Models (2022)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022, July). Evaluating Gaussian Grasp Maps for Generative Grasping Models. Presented at Proc. Int. Joint Conf. Neural Networks, Padova, Italy

Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the centre thir... Read More about Evaluating Gaussian Grasp Maps for Generative Grasping Models.

Statistical Power Grid Observability under Finite Blocklength (2022)
Presentation / Conference Contribution
Zhan, Q., Liu, N., Pan, Z., & Sun, H. (2022, May). Statistical Power Grid Observability under Finite Blocklength. Presented at 2022 3rd International Conference on Computing, Networks and Internet of Things, Qingdao, China

We study the stochastic observability of the power grid system under communication constraints in the finite blocklength regime. Compared to the study under the assumption of infinite blocklength, we introduce two new elements: probability of decodin... Read More about Statistical Power Grid Observability under Finite Blocklength.

Statistical Power Grid Observability under NOMA-based Communication Constraints (2022)
Presentation / Conference Contribution
Zhan, Q., Liu, N., Pan, Z., & Sun, H. (2022, May). Statistical Power Grid Observability under NOMA-based Communication Constraints. Presented at 2022 3rd International Conference on Wireless Communications and Big Data., Qingdao, China

This paper studies the observability of the power grid by jointly considering the power system with the wireless communication system under the strict latency requirements of Phasor Measurement Units (PMUs), which is characterized via the theory of e... Read More about Statistical Power Grid Observability under NOMA-based Communication Constraints.

Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems (2021)
Presentation / Conference Contribution
Hua, W., Jing, R., Zhou, Y., Zhang, X., Jiang, J., & Sun, H. (2023, November). Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems. Presented at 13th International Conference on Applied Energy (ICAE 2021), Bangkok

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.

Short-term load forecasting using artificial neural networks and social media data (2021)
Presentation / Conference Contribution
Boyd, A., Sun, H., Black, M., & Jesson, S. (2021, September). Short-term load forecasting using artificial neural networks and social media data. Presented at CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution, Online Conference

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.

Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul (2021)
Presentation / Conference Contribution
Maryopi, D., Yingjia, H., & Ikhlef, A. (2021, December). Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul. Presented at 2021 IEEE GLOBECOM Workshops, Madrid, Spain

The limited fronthaul capacity is known to be one of the main problems in cloud radio access networks (CRANs), especially in the wireless fronthaul links. In this paper, we consider the uplink of a CRAN system, where massive multiple-input multiple-o... Read More about Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul.

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.

Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres (2021)
Presentation / Conference Contribution
Mackie, M., Sun, H., & Jiang, J. (2021, October). Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres. Presented at IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) 2021, Espoo, Finland

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.

Autoencoders Without Reconstruction for Textural Anomaly Detection (2021)
Presentation / Conference Contribution
Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021, July). Autoencoders Without Reconstruction for Textural Anomaly Detection. Presented at 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China

Automatic anomaly detection in natural textures is a key component within quality control for a range of high-speed, high-yield manufacturing industries that rely on camera-based visual inspection techniques. Targeting anomaly detection through the u... Read More about Autoencoders Without Reconstruction for Textural Anomaly Detection.

A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming (2021)
Presentation / Conference Contribution
You, M., Zheng, G., & Sun, H. (2021, June). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. Presented at ICC 2021 - IEEE International Conference on Communications, Montreal, Quebec

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.

Trust-based Model for Securing Vehicular Networks Against RSU Attacks (2021)
Presentation / Conference Contribution
Alnasser, A., & Sun, H. (2021, May). Trust-based Model for Securing Vehicular Networks Against RSU Attacks. Presented at IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, BC

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.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021, January). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.

Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services (2020)
Presentation / Conference Contribution
Hernandez Cedillo, M., & Sun, H. (2020, November). Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services. Presented at 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ

The transition to a low carbon transportation system has brought many challenges for researchers, one major challenge is how to ensure power system reliability as a result of high load demands to supply energy to Electric Vehicles (EVs) while coping... Read More about Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services.