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Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques
Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort.
Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review
Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior.
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics.
Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings
In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time.
Parameters Optimization of Deep Learning Models using Particle Swarm Optimization
The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm.
