MCIR-CNN: Multichannel Imaging-Based Nonintrusive Load Identification With Rényi Entropy Window for Improved Sensor Data Processing

Non-intrusive load identification is an important aspect of load monitoring and can be accomplished using sensor data. In this paper, we propose MCIR-CNN, a novel load identification method that leverages multi-channel imaging of sensor time series data and convolutional neural networks (CNNs). We introduce a new method based on Rényi entropy for determining the sliding window size when segmenting sensor time series into subsequences, which we refer to as the Rényi entropy window (REW). Our method relies on sensor data to accurately classify loads, providing a non-intrusive solution for energy monitoring. We evaluate our method on publicly available datasets and show that MCIR-CNN outperforms the current state-of-the-art method, demonstrating the potential for sensors to improve household energy efficiency and reduce energy waste through load monitoring. By leveraging sensor data, our method has significant implications for the development of advanced sensor technologies in energy management.