In the maritime industry, sensors are utilised to implement condition-based maintenance
(CBM) to assist decision-making processes for energy efficient operations of marine …

Building energy prediction and management has become increasingly important in recent
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …

This study explores the applicability of data augmentation techniques for reconstructing
missing energy time-series in limited data regimes. In particular, multiple synthetic copies of …

Sensors deployed all over the buildings are nowadays collecting a large amount of data,
such as the Indoor Air Quality (IAQ) data which can provide valuable suggestions on …

Demand for electricity is gradually increasing in many countries. Efforts in related studies
have been made for the application of data mining techniques over related electric power …

Recently, the population density in cities has increased at a higher pace, so waste
generation is on the rise in most societies due to population growth. Given this concern, it …

Recently, in many existing buildings, detailed energy consumption data and various
indoor/outdoor environmental data could be collected by BEMS (Building energy …

In an Internet of Things (IoT) environment, sensors collect and send data to application
servers through IoT gateways. However, these data may be missing values due to …

As the number of installed meters in buildings increases, there is a growing number of data
time-series that could be used to develop data-driven models to support and optimize …

This article evaluates several machine learning methods to substitute the missing light
detection and ranging data for better spatial localization of industrial automated guided …