Predicting Building Energy Efficiency through Physics-Informed Neural Networks

Keskeiset käsitteet

A novel physics-informed neural network model for predicting building energy performance based on general building information and measured heating energy consumption.

Tiivistelmä

The paper introduces a data-driven, physics-informed approach for estimating the energy performance of residential buildings. The key highlights are:

The model predicts the properties of individual building envelope components (windows, doors, floors, roofs, basements) as well as the total energy consumption, going beyond typical energy consumption prediction.

The model incorporates a custom loss function that combines the error between model predictions and observed data with a physics-informed component based on linear heat loss equations.

The physics-informed function calculates the building's energy consumption based on the predicted envelope component properties and validates it against real energy consumption measurements.

Experiments on a dataset of 256 residential buildings in Riga, Latvia show promising results, with an R-squared of 0.87 and a normalized root mean squared error of 0.065 for total energy consumption prediction.

The methodology can be used by stakeholders like building owners, energy auditors, and policymakers to accelerate energy audits and inform targeted renovation initiatives.

Tilastot

The total energy consumption of the buildings ranged from 100 to 1200 kWh.
The area of the basement/slab ranged from 100 to 1000 m^2.
The U-value of the walls ranged from 0.2 to 0.6 W/(m^2·K).

Lainaukset

"The analytical prediction of building energy performance in residential buildings based on the heat losses of its individual envelope components is a challenging task."
"Our investigation comes up with promising results in terms of prediction accuracy, paving the way for automated, and data-driven energy efficiency performance prediction based on basic properties of the building, contrary to exhaustive energy efficiency audits led by humans, which are the current status quo."

Tärkeimmät oivallukset

Data-driven building energy efficiency prediction using physics-informed neural networks

by Vasilis Mich... klo arxiv.org 04-26-2024

https://arxiv.org/pdf/2311.08035.pdf

Syvällisempiä Kysymyksiä

How can the proposed methodology be extended to incorporate additional building-related variables beyond the envelope components to further improve the prediction accuracy

To enhance the prediction accuracy of the proposed methodology beyond the envelope components, additional building-related variables can be incorporated into the model. One approach is to include variables related to the building's HVAC system, such as the type of heating and cooling systems, ventilation rates, and energy efficiency ratings of these systems. By integrating these variables, the model can capture the impact of HVAC systems on energy consumption more accurately. Furthermore, factors like building occupancy patterns, usage schedules, and appliance energy consumption can also be included to provide a more comprehensive understanding of the building's energy performance. Additionally, incorporating weather data, such as temperature, humidity, and solar radiation, can help account for external factors influencing energy usage. By expanding the input variables to encompass a wider range of building-related factors, the model can offer more precise and holistic predictions of energy efficiency.

What are the potential limitations of relying solely on physics-informed equations within the loss function, and how could hybrid approaches combining multiple physical models be explored

Relying solely on physics-informed equations within the loss function may have limitations in capturing the complexity of real-world building energy dynamics. One potential limitation is the assumption of linear relationships between variables, which may not always hold true in practical scenarios. To address this, hybrid approaches that combine multiple physical models can be explored. By integrating both physics-based equations and data-driven machine learning techniques, the model can leverage the strengths of each approach. For instance, incorporating empirical data to calibrate the parameters of physics-based models can enhance the accuracy of predictions. Additionally, hybrid models can adapt to nonlinear relationships and complex interactions between variables, providing more robust and flexible predictions. By blending physics-informed equations with data-driven methods, the model can overcome the limitations of individual approaches and offer a more comprehensive and accurate analysis of building energy efficiency.

Given the heterogeneity in prediction accuracy across different envelope components, how could the model be adapted to provide more consistent and reliable estimates across all building elements

To address the heterogeneity in prediction accuracy across different envelope components, the model can be adapted by implementing component-specific optimization strategies. One approach is to assign different weights or importance factors to each envelope component based on their impact on overall energy consumption. By prioritizing components with higher energy loss contributions, the model can focus on improving predictions for critical areas of the building envelope. Additionally, fine-tuning the neural network architecture for each component, considering their unique characteristics and data distributions, can help enhance prediction consistency. Furthermore, incorporating ensemble learning techniques that combine predictions from multiple models trained on specific components can mitigate variability and improve overall prediction reliability. By tailoring the model's approach to address the specific challenges posed by different envelope components, a more consistent and reliable estimation of energy efficiency across all building elements can be achieved.