Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …

Although fully automated, operation of the District Heating Systems (DHS) is considered
reactive and simplistic since control decisions are most often made based on the real-time …

Forecasting wind speed has become one of the most attractive topics to researchers in the
field of renewable energy due to its use in generating clean energy, and the capacity for …

In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …

Despite the considerable success of deep learning methods in modelling physical
processes, they suffer from a variety of issues such as overfitting and lack of interpretability …

An electric power distribution utility is responsible for providing energy to consumers in a
continuous and stable way. Failures in the electrical power system reduce the reliability …

In the past decade, deep learning models have been applied to bio-sensors used in a body
sensor network for prediction. Given recent innovations in this field, the prediction accuracy …

In this paper we propose and discuss different Deep Learning-based ensemble algorithms
for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep …

Time series forecasting uses data from the past periods of time to predict future information,
which is of great significance in many applications. Existing time series forecasting methods …

According to the Industry 4.0 vision, humans in a smart factory, should be equipped with
formidable and seamless communication capabilities and integrated into a cyber-physical …