Neural Network for Energy Segmentation: WaveNILM Breaks Down Energy Consumption
In the realm of developing Smart Grid infrastructure, Non-Intrusive Load Monitoring (NILM) plays a crucial role. First proposed in 1992, NILM has been a seasoned research area, with Deep Learning taking the majority spotlight among various ways of approaching and solving its complex problems. One such Deep Learning architecture is WaveNILM, a promising solution for real-time disaggregation.
WaveNILM: A Convolutional Approach for Real-Time Disaggregation
WaveNILM, a Deep Learning architecture built on top of Causal Convolutional Neural Networks (CNNs), challenges Long Short-Term Memory (LSTMs) and Gated Recurrent Units (GRUs) in sequence modeling tasks. Its design is inspired by WaveNet architectures, utilising dilated convolutions to capture long-range dependencies efficiently.
This architecture has demonstrated strong accuracy in disaggregation tasks while maintaining relatively low computational complexity, which is critical for real-time applications. WaveNILM supports online, frame-by-frame processing without requiring future context, enabling causal (real-time) operation. Its efficiency lies in the use of convolutional operations and the absence of batch processing or heavy post-processing, making it well-suited for deployment in real-time systems with limited computational resources.
SSHMM: A Probabilistic Approach for NILM Disaggregation
SSHMM (Semi-Supervised Hidden Markov Model) uses hidden Markov models combined with semi-supervised learning to model appliance states and transitions. This probabilistic modeling approach explicitly incorporates state dynamics and temporal dependencies. SSHMM often achieves competitive accuracy, especially when labeled data is limited, by leveraging semi-supervised strategies.
However, the inference process in HMMs (especially with multiple appliances modeled jointly) can be computationally intensive due to state space explosion and recursive probabilistic computations. While HMMs can be implemented in real-time with certain approximations, the higher computational cost and complexity often make SSHMM less efficient for stringent real-time constraints compared to convolutional architectures like WaveNILM.
Comparing WaveNILM and SSHMM
In comparative studies and practical deployments focusing on real-time processing speed and scalability, WaveNILM tends to outperform SSHMM for NILM disaggregation. WaveNILM's convolutional design enables fast, causal inference ideal for embedded or resource-limited platforms. On the other hand, SSHMM can be more accurate in certain scenarios with limited labeled data but tends to be heavier computationally and less suited to hard real-time constraints.
In the study, WaveNILM was benchmarked against SSHMM architecture for noisy and denoised input signals, using a single dataset. Although popular datasets like REDD and Pecan Street were not utilised, the results indicate that WaveNILM offers a better balance between accuracy and computational efficiency for real-time NILM disaggregation.
WaveNILM's main advantage is its ability to add/remove the number of inputs for disaggregation for a better model fit, thanks to its use of a Gated Dilation method in its architecture. This flexibility makes it a versatile tool in the development of AI systems for both the consumer and the grid operator, contributing to the advancement of Power Disaggregation, a process used in building better grid infrastructures for increasing energy consumption.
For more information on WaveNILM and related studies, refer to the GitHub repository for WaveNILM implementation. The article provides several references to related studies and papers on Non-Intrusive Load Monitoring, Deep Learning, and Smart Grids.
Data-and-cloud-computing technologies can facilitate the deployment of WaveNILM and SSHMM architectures, two promising solutions for real-time NILM disaggregation. Artificial-intelligence,, particularly Deep Learning, plays a significant role in the design of these architectures, with WaveNILM employing Causal Convolutional Neural Networks and SSHMM using Hidden Markov Models.
