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Found 7 papers, 6 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.
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.
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.
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.
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
