Many stakeholders can beneﬁt from knowing the energy consumption of diﬀerent devices within a home. It helps users understand their bills, retailers to plan tariﬀ systems and distributors to plan network expansion. However, placing meters on all devices is expensive. Instead, we will determine device-level power consumption based on half-hourly aggregated data available from smart meters. For many devices, validation of power consumption can be detected visually by trained observers. This project will seek “ground truth” use of several device types by visually inspecting the estimates of state-of-the-art disaggregation techniques, and sub-metering a small number of homes.
This will allow the accuracy of the algorithms to be assessed and provide training data for more sophisticated supervised learning techniques.
The main objective of this project is to build NILM model based on Deep Neural Network Architectures. Stemming from this objective, the project aims to improve the accuracy of Non-Intrusive Load Monitoring (NILM) models in the context of Deep Neural Network architecture. Secondly, what reinforcement learning techniques can be used for this speciﬁc case of energy disaggregation.
Duration: 36 Months
Deadline to Apply: 19 January 2020
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