A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Energies, Journal Year: 2024, Volume and Issue: 17(3), P. 700 - 700

Published: Feb. 1, 2024

Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum uses is wide, but it evident that ML represents a revolution may change almost every human activity. However, for all innovations, comes with challenges. One the most critical challenges providing users an understanding how models’ output related to input data. This called “interpretability”, and focused on explaining what feature influences model’s output. Some have simple easy-to-understand relationship between output, while other models “black boxes” return without giving user information influenced it. lack this knowledge creates truthfulness issue when inspected by human, especially operator not data scientist. Building Construction sector starting face innovation, its scientific community working define best practices models. work intended developing deep analysis determine interpretable could be among promising future technologies energy management in built environments.

Language: Английский

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A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 204, P. 114804 - 114804

Published: Aug. 14, 2024

Language: Английский

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Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network

Applied Energy, Journal Year: 2023, Volume and Issue: 354, P. 122190 - 122190

Published: Nov. 8, 2023

Language: Английский

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AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory

Applied Energy, Journal Year: 2023, Volume and Issue: 356, P. 122334 - 122334

Published: Nov. 24, 2023

Language: Английский

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Performance assessment of cross office building energy prediction in the same region using the domain adversarial transfer learning strategy

Applied Thermal Engineering, Journal Year: 2024, Volume and Issue: 241, P. 122357 - 122357

Published: Jan. 5, 2024

Language: Английский

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Neural differential equations for temperature control in buildings under demand response programs

Applied Energy, Journal Year: 2024, Volume and Issue: 368, P. 123433 - 123433

Published: May 23, 2024

Heating Ventilation and Air Conditioning (HVAC) are energy-intensive systems that greatly contribute to peak demand, which can cause stability reliability issues in the grid. The use of adaptive smart temperature controllers combined with demand response programs play an important role addressing this challenge. However, deploying such is difficult, mainly due need for detailed models buildings be expensive acquire. This work proposes a general purpose approach alleviate problem through continuous-time neural differential equations predict HVAC power usage. A fully data-driven used train models, thus requiring no prior knowledge about apart from metering data. Therefore, easily adaptable different buildings. Furthermore, we show adapt incorporate high-level building physics further enhance their efficiency interpretability. planning algorithm embedded MPC framework designed control setpoint limit consumption increase eligibility program. Extensive empirical tests conducted on simulation real data assess each model's performance. experiments more sample-efficient robust missing irregular observations. Our also reveal same level accuracy, require fewer samples than discrete-time counterparts when adjusting setpoints lower during events.

Language: Английский

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Dyna-PINN: Physics-informed Deep Dyna-Q Reinforcement Learning for Intelligent Control of Building Heating System in Low-Diversity Training Data Regimes

Energy and Buildings, Journal Year: 2024, Volume and Issue: 324, P. 114879 - 114879

Published: Oct. 9, 2024

Language: Английский

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Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios

Applied Energy, Journal Year: 2023, Volume and Issue: 349, P. 121642 - 121642

Published: July 27, 2023

Language: Английский

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Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight

Energy and Buildings, Journal Year: 2024, Volume and Issue: 310, P. 114071 - 114071

Published: March 15, 2024

Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, typically required to reflect non-linear behavior of these anomalies. As an ongoing research question related Learning, a model's applicability limited settings can be explored introducing prior knowledge network. This same strategy also lead more interpretable predictions, hence facilitating field application approach. For that purpose, aim paper is propose use Physics-informed Denoising Autoencoders (PI-DAE) for missing imputation commercial buildings. particular, presented method enforces physics-inspired soft constraints loss function Autoencoder (DAE). order quantify benefits physical component, ablation study between different DAE configurations conducted. First, three univariate DAEs optimized separately on indoor air temperature, heating, cooling data. Then, two multivariate derived from previous configurations. Eventually, thermal balance equation coupled last configuration obtain PI-DAE. Additionally, commonly used benchmarks employed support findings. It shown how enhance inherent model interpretability through physics-based coefficients. While no significant improvement terms reconstruction error with proposed PI-DAE, its enhanced robustness varying rates valuable insights coefficients create opportunities wider applications within systems built environment.

Language: Английский

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Model-based investigation on building thermal mass utilization and flexibility enhancement of air conditioning loads

Building Simulation, Journal Year: 2024, Volume and Issue: 17(8), P. 1289 - 1308

Published: June 27, 2024

Language: Английский

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