CO2 concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data

P Linardatos, V Papastefanopoulos… - Scientific reports, 2023 - nature.com
Carbon Dioxide (CO 2) is a significant contributor to greenhouse gas emissions and one of
the main drivers behind global warming and climate change. In spite of the global economic …

Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities

V Papastefanopoulos, P Linardatos… - Smart Cities, 2023 - mdpi.com
Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …

Energy demand forecasting using deep learning

B Hrnjica, AD Mehr - Smart cities performability, cognition, & security, 2020 - Springer
Our cities face non-stop growth in population and infrastructures and require more energy
every day. Energy management is the key success for the smart cities concept since …

Automating energy demand modeling and forecasting using smart meter data

P Amin, L Cherkasova, R Aitken… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
The Internet of Things (IoT) technology with a variety of smart devices, communication
networks, and software systems for data processing is critical for optimizing Smart Grid …

The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability

SE Bibri - Sustainable cities and society, 2018 - Elsevier
Abstract The Internet of Things (IoT) is one of the key components of the ICT infrastructure of
smart sustainable cities as an emerging urban development approach due to its great …

Time Series Forecasting to Fill Missing Data in IoT Sensor Data

PD Rosero-Montalvo, P Tözün… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Internet of Things (IoT) devices have been deployed in different industrial applications due
to their feasible implementation thanks to the modern toolsets and many sensor brands to …

Time series forecasting for improving quality of life and ecosystem services in smart cities

R López-Blanco, JH Martín, RS Alonso… - … Symposium on Ambient …, 2022 - Springer
Quality of life is one of the factors that most influence the mood of citizens. As many studies
have shown, one of the ways to increase the perception of quality of life are the actions on …

Air quality forecasting based on gated recurrent long short term memory model in Internet of Things

B Wang, W Kong, H Guan, NN Xiong - IEEE Access, 2019 - ieeexplore.ieee.org
With the continuous development of the Chinese economy and the gradual acceleration of
urbanization, it has caused tremendous damage to the environment. The bad air …

A high spatiotemporal resolution framework for urban temperature prediction using IoT data

J Yang, M Yu, Q Liu, Y Li, DQ Duffy, C Yang - Computers & Geosciences, 2022 - Elsevier
Accurate weather prediction, particularly accurate temperature prediction, is critical in
decision-making for energy consumption, health risks, and economics. Regional numerical …

A predictive analytics framework for sensor data using time series and deep learning techniques

HA Selmy, HK Mohamed, W Medhat - Neural Computing and Applications, 2024 - Springer
IoT devices convert billions of objects into data-generating entities, enabling them to report
status and interact with their surroundings. This data comes in various formats, like …