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Found 22 papers, 15 papers with code
Creating synthetic energy meter data using conditional diffusion and building metadata
Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata.
Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight
While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment.
Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas
The masks generated using the segmentation models were then used to extract the temperature from thermal images and correct for differences in the emissivity of various urban features.
BIM-to-BRICK: Using graph modeling for IoT/BMS and spatial semantic data interoperability within digital data models of buildings
The automation of this approach can be compared to traditional manual mapping of data types.
Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation
Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data.
District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images
The rooftop infrared thermography observatory with a multi-modal platform that is capable of assessing a wide range of dynamic processes in urban systems was deployed in Singapore.
Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterization
It is observed that for the water-cooled system, the difference between the rate of change of the window and wall can be used to extract the operational pattern.
InfraRed Investigation in Singapore (IRIS) Observatory: Urban heat island contributors and mitigators analysis using neighborhood-scale thermal imaging
From data collected by the observatory and the automatic weather station network, a method was developed to estimate the heat emitted by building facades, vegetation, and traffic.
Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data
On the other hand, for half and one third of each dataset occupant population, using Cohort Comfort Models, with less historical data from target occupants, Cohort Comfort Models increased their thermal preference prediction by 8~\% and 5~\% on average, and up to 36~\% and 46~\% for some occupants, when compared to general-purpose models trained on the whole population of occupants.
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles
Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction.
Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models
Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them.
Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned
The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late 2019 as one of the largest machine learning competitions ever held focused on building performance.
Using Google Trends as a proxy for occupant behavior to predict building energy consumption
In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research.
Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec
Build2Vec utilizes the spatial data from the Building Information Model (BIM) and indoor localization in a real-world setting.
Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis
Higher magnitude (out-of-range) errors (RMSLE_scaled > 0. 3) occur in 4. 8% of the test data and are unlikely to be accurately predicted.
Balancing thermal comfort datasets: We GAN, but should we?
However, when classes representing discomfort are merged and reduced to three, better imbalanced performance is expected, and the additional increase in performance by $\texttt{comfortGAN}$ shrinks to 1-2%.
The ASHRAE Great Energy Predictor III competition: Overview and results
This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s.
Computers and Society
Humans-as-a-sensor for buildings: Intensive longitudinal indoor comfort models
These classification models were trained on a feature set that was developed from time-series attributes, environmental and near-body sensors, heart rate, and the historical preferences of both the individual and the comfort group assigned.
Human-Computer Interaction Applications
Build2Vec: Building Representation in Vector Space
In this paper, we represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs obtained from a Building Information Model (BIM).
Spacematch: Using environmental preferences to match occupants to suitable activity-based workspaces
In this work, we tested the ability for this feedback data to be merged with indoor environmental values from Internet-of-Things (IoT) sensors to optimize space and energy use by grouping occupants with similar preferences.
The Building Data Genome Project 2: Hourly energy meter data from the ASHRAE Great Energy Predictor III competition
This paper describes an open data set of 3, 053 energy meters from 1, 636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17, 544 measurements per meter resulting in approximately 53. 6 million measurements).
Applications
EnergyStar++: Towards more accurate and explanatory building energy benchmarking
Even more importantly, a set of techniques is developed to help determine which factors most influence the score using SHAP values.
