The current digitalisation revolution demonstrates the high importance and possibilities of quality data in industrial applications. Data represent the foundation of any analytical process, establishing the fundamentals of the modern Industry 4.0 era. Data-driven processes boosted by novel Artificial Intelligence (AI) provide powerful solutions for industrial applications in anomaly detection, predictive maintenance, optimal process control and digital twins, among many others. Virtual Sensors offer a digital definition of a real Internet of Things (IoT) sensor device, providing a smart tool capable to face key issues on the critical data generation side: i) Scalability of expensive measurement devices, ii) Robustness and resilience through real-time data validation and real-time sensor replacement for continuous service, or iii) Provision of key parameters’ estimation on difficult to measure situations. This chapter presents a profound introduction to Virtual Sensors, including the explanation of the methodology used in industrial data-driven projects, novel AI techniques for their implementation and real use cases in the Industry 4.0 context.

Nowadays, water utilities face the rising challenge of ensuring water availability amidst a rapidly growing society and a shifting climate. Our research aims to develop a household clustering solution based on water consumption behaviour in Southwest England, to enable utilities to identify different profiles and enhance customized control of household consumption, resulting in improved resource management. The solution is composed of three modules. The first one is based on a K-Means clustering model, designed to group domestic water use behaviours. This module uses the Dynamic Time Wrapping algorithm as a similarity mechanism to process the high-resolution water meter data. In parallel, the second module processes the market segmentation data through an Autoencoder, a specific Neural Network architecture used to reduce the high dimensionality of such data to a low dimension dataset by extracting its latent encoded space. Finally, to assemble the final household water use profiles, a blending K-Means algorithm is used to merge previous modules outputs, based on the Euclidean distance. The solution provides insightful information to water companies to better understand consumer behaviour, habits, and routines.

The prediction of sediment levels in combined sewer system (CSS) would result in enormous savings in resources for their maintenance as a reduced number of inspections would be needed. In this paper, we benchmark different machine learning (ML) methodologies to improve the maintenance schedules of the sewerage and reduce the number of cleanings using historical sediment level and inspection data of the combined sewer system in the city of Barcelona. Two ML methodologies involve the use of spatial features for sediment prediction at critical sections of the sewer, where the cost of maintenance is high because of the dangerous access; one uses a regression model to predict the sediment level of a section, and the other one a binary classification model to identify whether or not a section needs cleaning. The last ML methodology is a short-term forecast of the possible sediment level in future days to improve the ability of operators to react and solve an imminent sediment level increase. Our study concludes with three different models. The spatial and short-term regression methodologies accomplished the best results with Artificial Neural Networks (ANN) with 0.76 and 0.61 R2 scores, respectively. The classification methodology resulted in a Gradient Boosting (GB) model with an accuracy score of 0.88 and an area under the curve (AUC) of 0.909.

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