Microfluidic devices are used to transfer small quantities of liquid through micro-scale channels. Conventionally, these devices are fabricated using techniques such as soft-lithography, paper microfluidics, micromachining, injection moulding, etc. The advancement in modern additive manufacturing methods is making three dimensional printing (3DP) a promising platform for the fabrication of microfluidic devices. Particularly, the availability of low-cost desktop 3D printers can produce inexpensive microfluidic devices in fast turnaround times. In this paper, we explore fused deposition modelling (FDM) to print non-transparent and closed internal micro features of in-plane microchannels (i.e., linear, curved and spiral channel profiles) and varying cross-section microchannels in the build direction (i.e., helical microchannel). The study provides a comparison of the minimum possible diameter size, the maximum possible fluid flow-rate without leakage, and absorption through the straight, curved, spiral and helical microchannels along with the printing accuracy of the FDM process for two low-cost desktop printers. Moreover, we highlight the geometry dependent printing issues of microchannels, pressure developed in the microchannels for complex geometry and establish that the profiles in which flowrate generates 4000 Pa are susceptible to leakages when no pre or post processing in the FDM printed parts is employed.

Non-intrusive load monitoring (NILM) is widely regarded as a key method for energy disaggregation to estimate the energy consumption and operating time of individual equipment and appliances. Over the past few years, NILM has achieved substantially high accuracy through the incorporation of several machine learning (ML) and deep learning (DL) algorithms. This article reviews ML/DL-based approaches that have been applied to address NILM-related problems. This review focuses on five research questions: First, which problems associated with NILM systems have been addressed in recent studies using ML and DL models, and how well have the ML and DL models addressed NILM-related problems? Second, which ML and DL models have been the most explored for NILM, and how do the ML and DL algorithms perform on different NILM datasets? Third, how has model interpretability been addressed in ML/DL-based NILM methods, and what techniques have been proposed to improve the transparency of predictions? Fourth, what are the practical requirements of ML-based NILM systems in real-world settings? Finally, what are the main challenges of ML-based NILM systems in terms of datasets, models, and their implementation? This review also provides recommendations for addressing the identified research gaps and emerging trends and technologies in NILM and presents directions for future research.

Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.