Deep Learning Techniques for Edge-Centric Non-Intrusive Load Monitoring

Climate change is one of the most significant challenges faced in this century. To limit the rise in global average temperatures below 1.5\,°C, it is crucial to decrease the electrical energy usage. Therefore, it is vital to promote energy efficiency through sustainable practices. Devices should be used efficiently to avoid energy waste. In the residential setting, individuals can significantly contribute to energy saving, especially if they are aware of their consumption. The constant availability of energy consumption profiles has led to the development of advanced techniques to monitor loads inside buildings and provide residential users with improved awareness of their energy consumption and usage habits. One such technique is Non-Intrusive Load Monitoring (NILM), which detects the states of appliances and estimates the power consumption of individual loads in the building based only on the building’s aggregate meter readings. Nowadays, most of the approaches proposed in the literature are based on deep learning, which has proven superior to other methods. Nonetheless, they still have to deal with aspects related to real-world applicability. Firstly, there is the issue of the availability of labeled datasets. Labels should be provided by annotators, who are often end-user, but this process is time-consuming and prone to errors. Second, computation is usually done in the cloud, which is far from where the data are acquired. This requires data transmission and can result in latency in the service output. To mitigate the above issues, this thesis proposes several methodologies that follow the Human-Centred Computing and the Edge Computing paradigms. As a consequence, the developed strategies aim to lighten the effort requested to the user for providing labels while enhancing the performance. At the same time, the computation is lightened by reducing algorithms complexity while maintaining performance. The methods have been developed and evaluated on publicly available datasets, demonstrating their superiority compared to benchmark strategies. Moreover, the final performance is increased, even with less data and simpler structures. Future directions considers to train networks locally to promote adaptability and reliability. Additionally, hybrid monitoring strategies can be investigated and integrated with energy management systems or demand-response programs based on the user requirements.