Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid

Abstract

In the current era of Internet of Things (IoT) devices, household solar panels, and increasingly aordable local energy storage, energy grid systems are facing a new set of challenges that they were not originally designed to support. Energy systems of the near future must be capable of supporting these new technologies, but new technology can also be leveraged to improve reliability and eciency overall. A major source of potential improvements comes from the increase of connected devices that are capable of dynamically adjusting their behavior, and offer new data that can be used for optimization and prediction. Energy predictions are used today at the bulk power system level to ensure demand is met through appropriate resource allocation. As energy systems become more responsive, prediction will be important at more granular system levels and timescales.

Enabled by the rise in available data, existing research has shown some machine learning models to be superior to traditional statistical models in predicting long-term aggregate usage. However, these models tend to be computationally expensive; if machine learning prediction models are to be used at short timescales and performed close to the end nodes, there is a need for more ecient models. Additionally, most machine learning models today do not take advantage of the known and studied properties of the underlying energy data. This thesis explores the circumstances under which machine learning can be used to make predictions more accurately than existing methods, and how machine learning and statistical methods can serve to complement each other (specically for short timescales at the household level).

We nd that basic machine learning models outperform other baseline and statistical models by using energy usage trends observed from statistical methods to better engineer the input features. For the increasingly distributed energy systems that these predictive models aim to support, the distributed nature of blockchain technology has been proposed as a good match for managing such systems. As an example of one possible distributed management implementation, this thesis presents a novel blockchain-enabled architecture that provides privacy for users, information security through improved household-level prediction, and takes into consideration the security vulnerabilities and computational constraints of the participants.