Accepted Papers Abstracts: [BuildSys’20] SolarTrader: Enabling Distributed Solar Energy Trading in Residential Virtual Power Plants. By Yuzhou Feng, Qi Li, Dong Chen, and Raju Rangaswami. We propose SolarTrader, a new solar energy trading system that enables unsupervised, distributed, and long term fair solar energy trading in residential VPPs. In essence, SolarTrader leverages a new multi-agent deep reinforcement learning approach that enables peer-to-peer solar energy trading among different DSERs to ensure that both the DSER users and the VPPs maximize benefit. We implement SolarTrader and evaluate it using both synthetic and real smart meter data from 4 U.S. residential VPP communities that are comprised of ~229 residential DSERs in total. Our results show that SolarTrader can reduce the aggregated VPP energy consumption by 83.8% when compared against a non-trading approach. Furthermore, SolarTrader achieves a ~105% average saving in VPP residents’ monthly electricity cost. We also find that SolarTrader-enabled VPPs can achieve a fairness of 0.05, as measured by the Gini Coefficient, a level equivalent to that achieved by the fairness-maximizing Round-Robin approach.
[IGSC’20] SolarDiagnostics: Automatic Rooftop Solar Photovoltaic Array Damage Detection. By Qi Li, Keyang Yu and Dong Chen. We find that SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews Correlation Coefficient (MCC) of 1.0. In addition, pre-trained SolarDiagnostics yields an MCC of 0.95, which is significantly better than other re-trained machine learning-based approaches and yields as the similar MCC as of re-trained SolarDiagnostics. We make the source code and datasets that we use to build and evaluate SolarDiagnostics publicly-available.
[BigDataCPS’20] SmartAttack: Open-source Attack Models for Enabling Security Research in Smart Homes. By Qi Li, Keyang Yu and Dong Chen. The Internet of Things (IoT) has been erupting the world widely over the decade. Smart home and smart building owners are increasingly deploying IoT devices to monitor and control their environments due to the rapid decline in the price of IoT devices. The recent intensive research has shown that the network traffic traces of IoT devices have significant cybersecurity and privacy issues. These security and privacy defending techniques have enabled sophisticated approaches to ensure security and preserve user privacy. However, due to the fact that different approaches are evaluated using their own datasets, their own developed security and privacy attack models, and their own evaluating metrics, it is being significantly difficult to make a fair and comprehensive comparisons among different IoT security strengthening and user privacy preserving research to better understand IoT security issues and end user benefits.To address this problem, we present a deep learning-based adversarial attack model framework SmartAttack, which enables a set of general sophisticated adversarial attack models that can be leveraged by researchers and industrial users from IoT security community to better evaluate their work.