Welcome to the NTA 8800 Energylabel v2 API documentation. This API uses a combination of various sources and methods to deliver the most accurate energy label estimates possible.
Introduction
The NTA 8800 Energylabel API v2 estimates the current energy performance of Dutch properties using the NTA 8800 methodology, the official Dutch standard for calculating building energy labels.
The API enables users to estimate the current energy label of a property and retrieve the sustainability measures currently present in the building. It supports both minimal address input and detailed building characteristics, allowing users to balance speed and accuracy depending on their use case.
Typical applications include sustainability platforms, real estate tools, property valuation models, and financial applications that require scalable insights into building energy performance.
Core Capabilities
Estimated Energy Label Calculation
The API estimates the current energy label of a property based on available building data and predictive models aligned with the NTA 8800 framework.
The result includes:
Estimated energy label (A++++ → G)
Estimated BENG2 score (primary fossil energy usage)
Estimated energy consumption values
Estimated usages (gas, energy, city heating) and CO2
Relevant sustainability indicators
These results provide a quick indication of the current energy efficiency level of a building.
Range of Energy labels
Detection of Existing Energy Measures
The API also estimates which energy efficiency measures are currently present in the building.
Examples include:
Wall insulation
Roof insulation
Floor insulation
Window glazing types
Heating installation type
Ventilation systems
Solar panels
These measures form the basis for the estimated energy performance calculation.
Flexible Input Model
The API is designed to operate with different levels of input detail.
Minimal Input
Users can retrieve an energy performance estimate by providing only:
Postal code
House number
House addition
Based on this information, the API estimates building characteristics using proprietary datasets and statistical models.
Detailed Building Input
For higher accuracy, users may provide additional information such as:
House type
Build year
Inner surface area
Insulation levels
Heating installation
Solar panels
Ventilation systems
Providing more detailed building characteristics reduces model assumptions and improves the accuracy of the estimated energy label.
Accuracy and Methodology
The API estimates the current energy label using a combination of statistical modelling, machine learning, and NTA 8800-based calculations.
Because complete building data is often unavailable, the model focuses on the most influential building characteristics and uses predictive models trained on large datasets of Dutch residential properties.
Machine Learning Prediction
When direct data about a property is unavailable, the API uses machine learning models to estimate building characteristics and energy performance indicators.
