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The current energy crisis has revealed deep vulnerabilities in energy supplies It also emphasised the strong fossil fuel dependency of many countries Some governments decided to tighten their energy policies particularly on energy e iciency and the use of intermittent and renewable energies Photovoltaic energy has the advantage of emitting few greenhouse gases in its lifetime operation but has the drawback of being intermittent and variable Energy storage can mitigate the variability and intermittency of solar energy However energy storage s substantial environmental and financial costs may refrain from investing massively A second way to face renewable drawbacks involves more intelligent demand management Developing a Home Energy Management System HEMS for residential areas has been a growing research trend but has not been widely applied due to technical social and financial barriers It is expected to monitor appliances individually in residential areas to understand the user s behaviours accurately to determine the preferred hour of use of each appliance the duration or the energy demanded However Appliance Load Monitoring ALM is challenging due to the high complexity and cost of the sensor network involved Non-Intrusive Load Monitoring NILM is the process of disaggregating the main load into individual appliance loads NILM aims to mitigate the cost of sensor installation and maintenance by installing a unique sensor on the main load and retrieving the individual load appliances computationally Recently deep learning models have been up-and-coming for NILM tasks The main meter sampling rate is crucial for the trade-off between disaggregation accuracy and cost e iciency High-frequency data will carry more detailed signatures but with a costly sensor In this work we assess the generalisation capabilities of low-frequency NILM and identify the settings having the most significant impact on performances This work demonstrates the importance of diversity in the training dataset to unlock generalisability At the same time the literature underlines data scarcity in the domain mainly due to the complex task of recording reliable supervised data in real conditions The present work provides a new data augmentation technique to address this issue by enriching the training set The technique is thoroughly experimented with to prove its e iciency in improving generalisability The data augmentation add-on module is implemented inside the NILMTK framework a famous toolkit to evaluate NILM solutions This manuscript brought particular care to a practical NILM application The idea is to assess the contribution of a low-frequency NILM to a HEMS by conducting simulations The case study applies a load scheduling HEMS to real houses from public databases The schedules given by the HEMS with NILM are more acceptable for the end-user The first original contribution of this work original relies on developing a new and straightforward data augmentation technique published in the SEGAN Sustainable Energy Grids and Network journal Secondly the thesis gives an original approach to quantitatively evaluating NILM s contribution to HEMS

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Contribution of Non-Intrusive Load Monitoring to Home Energy Management Systems

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