Securing Smart Grids with Machine Learning

Making Smart Grids Smarter by Using Machine Learning

The huge amounts of data required by Smart grids operation are impossible to be processed by human operators in a timely manner. New intelligent systems should provide a clear decision regarding the system state. This paper proposes a new methodology based on supervised learning using AdaBoost and CARTs as decision support system for power system state classification. The methodology proves to ...

Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach

In smart cities, advanced metering infrastructure (AMI) of the smart grid facilitates automated metering, control and monitoring of power distribution by employing a wireless network. Due to this wireless nature of communication, there exist potential threats to the data privacy in AMI. Decoding the energy consumption reading, injecting false data/command signals and jamming the networks are so...

Securing Machine Learning Classifiers with Input Hashing Re-weight Strategy

Adversarial machine learning resides at the intersection of machine learning and computer security. Originally, machine learning techniques were designed for environments that do not assume the presence of an adversary. However, in the presence of intelligent adversaries, this working hypothesis is likely to be violated to at least to some degree, depending on the skillset of an adversary. A sk...

Smart Grids

Smart - Grids

We build a system dynamics model to evaluate the potential dynamics of consumer adoption of “Smart Energy Behaviour”. Within this term we include different levels of: i) shift in electricity consumption towards less costly-less polluting and congestioning hours; ii) the reduction of mainly wasteful electricity consumption, that maintains similar levels of comfort; iii) the enrolment in demand r...