Synthetic Data for Non-Intrusive Load Monitoring: A Markov Chain Based Approach

This paper deals with the generation of synthetic data, which plays an important role in the Non-Intrusive Load Monitoring (NILM) problem. We introduce the NILM problem and then explain its crucial role in improving energy efficiency and supporting smart grid functions. The paper explains the stages of the NILM problem, including data acquisition, feature extraction, event detection, and appliance classification. We also explain two methods for generating synthetic data: AMBAL (Appliance Model Based Algorithm for Load monitoring) and SmartSim. Then, we propose a synthetic data generation method based on Markov chains, which is designed to generate labeled data useful for training supervised machine learning models. The proposed method utilizes the probabilistic transitions between different operational states of appliances, and captures the stochastic nature of real-world appliance usage. Thus, the generated synthetic data not only reflects realistic usage patterns, but also contains labels indicating the state of each appliance at a given time. The simulations are then run by generating synthetic data for typical office equipment such as laptops and televisions. The generated data sets provide detailed and accurate usage profiles, which are important for the effective training and validation of NILM algorithms. Since the generated data also includes the labeled data, this method will improve the ability of NILM systems to accurately identify and monitor individual appliances in a complex load environment.