Concetti Chiave
A novel physics-informed neural network model for predicting building energy performance based on general building information and measured heating energy consumption.
Sintesi
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.
Statistiche
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).
Citazioni
"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."