Temperature Forecasting

Temperature forecasting, particularly within buildings, aims to accurately predict indoor temperatures to optimize energy consumption and improve occupant comfort in HVAC systems. Current research emphasizes data-driven approaches, employing machine learning models like transformers and leveraging techniques such as synthetic data augmentation to address data scarcity and improve model robustness across diverse scenarios, including those with limited or unusual data. These advancements are crucial for efficient energy management in buildings, reducing operational costs and environmental impact, and improving the accuracy and reliability of building energy management systems.

Papers

September 1, 2024

SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation
Luigi Capogrosso, Andrea Toaiari, Andrea Avogaro, Uzair Khan, Aditya Jivoji, Franco Fummi, Marco Cristani
Trajectory Prediction Geometric Feature Self Supervised Visual Representation Trajectory Forecasting Temperature Forecasting

June 7, 2024

Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments
Zachari Thiry, Massimiliano Ruocco, Alessandro Nocente, Michail Spitieris
Synthetic Data Synthetic Data Generation Environment Feature Data Augmentation Technique Synthetic Data Augmentation Synthetic Data Generator Temperature Forecasting

February 21, 2024

Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering
Dafang Zhao, Zheng Chen, Zhengmao Li, Xiaolei Yuan, Ittetsu Taniguchi
K Mean Data Driven Approach Energy Management Heating, Ventilation, and Air Conditioning Temperature Forecasting

November 29, 2023

The Forecastability of Underlying Building Electricity Demand from Time Series Data
Mohamad Khalil, A. Stephen McGough, Hussain Kazmi, Sara Walker
Data Driven Time Series Data State of the Art Forecasting Electricity Demand Building Energy Temperature Forecasting

October 31, 2023

Global Transformer Architecture for Indoor Room Temperature Forecasting
Alfredo V Clemente, Alessandro Nocente, Massimiliano Ruocco
Transformer Architecture Local Transformer Multi Room Temperature Prediction Temperature Forecasting

June 10, 2023

Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
Arian Prabowo, Kaixuan Chen, Hao Xue, Subbu Sethuvenkatraman, Flora D. Salim
Distribution Data Human Mobility SpatioTemporal Data Occupancy Information Temperature Forecasting

August 19, 2022

A physics-based domain adaptation framework for modelling and forecasting building energy systems
Zack Xuereb Conti, Ruchi Choudhary, Luca Magri
Physic Informed Neural Network Physical Reasoning Mechanistic Model Temperature Forecasting Heat Conduction

Temperature Forecasting

Papers

SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation

Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments

Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering

The Forecastability of Underlying Building Electricity Demand from Time Series Data

Global Transformer Architecture for Indoor Room Temperature Forecasting

Continually learning out-of-distribution spatiotemporal data for robust energy forecasting

A physics-based domain adaptation framework for modelling and forecasting building energy systems