5 citations · 6 across the 5 of their papers we have counts for
5 papers
Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments
Zachari Thiry, Massimiliano Ruocco, Alessandro Nocente +1
Forecasting indoor temperatures is important to achieve efficient control of HVAC systems. In this task, the limited data availability presents a challenge as most of the available…
Configurable convolutional neural networks for real-time pedestrian-level wind prediction in urban environments
Alfredo Vicente Clemente, Knut Erik Teigen Giljarhus, Luca Oggiano +1
Urbanization has underscored the importance of understanding the pedestrian wind environment in urban and architectural design contexts. Pedestrian Wind Comfort (PWC) focuses on th…
Global Transformer Architecture for Indoor Room Temperature Forecasting
Alfredo V Clemente, Alessandro Nocente, Massimiliano Ruocco
A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a build…
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series
Sondre Sørbø, Massimiliano Ruocco
The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific do…
Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting
Espen Haugsdal, Erlend Aune, Massimiliano Ruocco
Time series forecasting is an important problem, with many real world applications. Ensembles of deep neural networks have recently achieved impressive forecasting accuracy, but su…