Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters & Modular Pipelines

Non-Intrusive Load Monitoring (NILM) is a technique used to monitor the energy usage of individual appliances and devices in a home or building, without the need to physically measure each appliance or device. This allows energy managers to more accurately understand how energy is being used in the building. The basic principle behind NILM is to measure the overall energy usage of the building, and then identify patterns in the usage that can be attributed to specific appliances or devices. By analyzing the total energy usage, NILM can identify the type of appliance and its energy consumption. This information can then be used to make informed decisions about energy management, such as identifying energy-efficient appliances and optimizing energy usage. NILM is important for energy management applications because it provides a more comprehensive view of energy use. By understanding the energy usage of individual devices, energy managers can make better decisions about how to optimize energy usage and reduce energy costs. Furthermore, NILM can identify potential problems in the system, such as inefficient appliances, which can be addressed in order to improve efficiency.

Overview of the NILM pipeline in Deep-NILMTK

In recent years, Non-Intrusive Load Monitoring (NILM) has become an important tool for identifying the power consumption of individual appliances from a single metering point. Deep learning models are gaining traction in this area, however, there are still many challenges surrounding NILM datasets and the lack of common experimental guidelines. This lack of features and best practices guidelines has limited the adoption of efficient research instruments and made it difficult to compare, replicate, and share results.

To address this problem, we have proposed a novel open-source toolkit, Deep-NILMTK, which leverages the best practices for Deep Learning and offers a common testing bed for NILM algorithms. This toolkit includes a modular NILM pipeline that can be easily customised and introduces the concept of Experiment Templating to improve research efficiency. To demonstrate the effectiveness of the tool, we have created an online NILM benchmark repository and conducted a case-study with eight of the most popular deep NILM algorithms. All sources for the tool are available on Github, along with the accompanying documentation.

Leveraging this concept and DL best practices, a case-study of creating an online NILM benchmark repository is provided at https://github.com/BHafsa/DNN-NILM-benchmark considering eight of the most popular deep NILM algorithms. All sources relative to the tool are publicly available on Github https://github.com/BHafsa/deep-nilmtk-v1 along with the corresponding documentation.

Further information can be found in the paper

Hafsa Bousbiat, Anthony Faustine, Christoph Klemenjak, Lucas Pereira, and Wilfried Elmenreich. Unlocking the full potential of neural NILM: On automation, hyperparameters & modular pipelines. IEEE Transactions on Industrial Informatics, pages 1–9, 9 2022. (doi:10.1109/TII.2022.3206322)

 

Neural NILM Learning Paradigms: From Centralised to Decentralised Learning

Centralised vs collaborative learning

Non-intrusive Load Monitoring (NILM) has become a paramount in both industrial and residential sectors to achieve efficient energy consumption. Deep neural networks have been gaining the highest interest from the research community, commonly referred to as neural NILM. In most cases, neural NILM models follow a centralised based learning scheme, where the energy data is assumed to be available in a central node for training. This practice can, however, raise privacy and security concerns from the consumer’s side since energy data can reveal in-home activities and occupancy records if intercepted. In response, Federated Learning (FL) has been suggested as a viable solution to address these issues. In the paper "Neural NILM Learning Paradigms: From Centralised to Decentralised Learning", an overview of neural NILM models following both a centralised and a federated learning paradigm was presented while also identifying the main challenges with regard to both learning paradigms and potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry. Overall, as any other new technology, FL has its merits and limitations. Typically, FL provides promising perspectives to solve the privacy issues of energy disaggregation. However, it also opens doors for new challenges, especially those related to the (i) low disaggregation performance of FL-based NILM algorithms, (ii) susceptibility to noise, (iii) lack of labeled sub-metered data at the customer’s level, and (iv) need to adopt robust security mechanisms.

Further information can be found in the paper:

Hafsa Bousbiat, Christoph Klemenjak, Yassine Himeur, Wilfried Elmenreich, Abbes Amira, Wathiq Mansoor, and Shadi Atalla. Neural NILM learning paradigms: From centralised to decentralised learning. In Proceedings of the 2022 5th International Conference on Signal Processing and Information Security (ICSPIS), pages 138–142, December 2022. (doi:10.1109/icspis57063.2022.10002485)

The paper also won the best paper award at the 5th International Conference on Signal Processing and Information Security (ICSPIS) in December 2012.