NTA 8800 Energylabel API

Welcome to the NTA 8800 Energylabel 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 is designed to equip users with the ability to estimate a property's current or potential energy efficiency rating based on specific property details. This API serves as a robust analytical tool, providing two core functionalities:

1. Generate an estimated current energylabel: Users can input data regarding a property's existing characteristics to receive an estimated energy rating according to the NTA 8800 standard. This estimation helps in gauging the current energy performance of a property.

2. Retrieve current estimated measures present in the property: The API also offers insights into the energy efficiency measures already implemented within a property. This allows for a comprehensive understanding of the property's existing energy-saving features.

By leveraging the NTA 8800 EnergyLabel API, users can obtain a clear picture of a property's energy efficiency status and explore potential improvements to enhance its energy performance.

Range of Energy labels

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Determine the estimated current or potential NTA 8800 energylabel based on inputs on the current or expected situation of the property

Our latest accuracy metrics are available and have been validated against multiple datasets. The performance of our estimates has been thoroughly assessed to ensure reliability. You can find detailed accuracy information below:

Measure Period: Q2 2024

Altum AI recently conducted performance testing on the NTA 8800 Energylabel API using a dataset of 4.220 addresses with the NTA 8800 determination method. The purpose of this testing was to evaluate the BENG2 accuracy of the NTA 8800 Energylabel API when handling address-related queries. The results of the testing are as follows:

• Mean Absolute Error: 8.8

• Median Absolute Error: 1.0

These metrics provide insights into the average and median deviation between predicted and actual BENG2 values. Lower values indicate higher accuracy and precision in our API's performance. In addition to these metrics, we have also analyzed the predictions within various error ranges:

• BENG2

  - 79.79% predictions in 5 BENG2 units error range

  - 84.43% predictions in 10 BENG2 units error range

  - 91.09% predictions in 20 BENG2 units error range

  - 94.57% predictions in 30 BENG2 units error range

  - 96.71% predictions in 40 BENG2 units error range

  - 97.56% predictions in 50 BENG2 units error range

  - 97.89% predictions in 60 BENG2 units error range

  - 98.01% predictions in 70 BENG2 units error range

  - 98.29% predictions in 80 BENG2 units error range

  - 98.44% predictions in 90 BENG2 units error range

  - 98.67% predictions in 100 BENG2 units error range

• Energy label

  - 99.62% predictions in 0 energy label difference

  - 99.83% predictions in 1 energy label difference

  - 99.95% predictions in 2 energy label difference

  - 100.00% predictions in 3 energy label difference

  - 100.00% predictions in 4 energy label difference

  - 100.00% predictions in 5 energy label difference

These percentages indicate the accuracy of our API's predictions within specific error ranges. Higher percentages demonstrate the reliability and consistency of our API's performance

Measure Period: Q1 2024

Altum AI recently conducted performance testing on the NTA 8800 Energylabel API using a dataset of 4.408 addresses with the NTA 8800 determination method. The purpose of this testing was to evaluate the BENG2 accuracy of the NTA 8800 Energylabel API when handling address-related queries. The results of the testing are as follows:

• Mean Absolute Error: 8.69

• Median Absolute Error: 1.0

These metrics provide insights into the average and median deviation between predicted and actual BENG2 values. Lower values indicate higher accuracy and precision in our API's performance. In addition to these metrics, we have also analyzed the predictions within various error ranges:

• BENG2

  - 80.31% predictions in 5 BENG2 units error range

  - 85.96% predictions in 10 BENG2 units error range

  - 91.58% predictions in 20 BENG2 units error range

  - 94.92% predictions in 30 BENG2 units error range

  - 96.87% predictions in 40 BENG2 units error range

  - 97.69% predictions in 50 BENG2 units error range

  - 97.96% predictions in 60 BENG2 units error range

  - 98.07% predictions in 70 BENG2 units error range

  - 98.34% predictions in 80 BENG2 units error range

  - 98.48% predictions in 90 BENG2 units error range

  - 98.73% predictions in 100 BENG2 units error range

• Energy label

  - 99.64% predictions in 0 energy label difference

  - 99.84% predictions in 1 energy label difference

  - 99.95% predictions in 2 energy label difference

  - 100.00% predictions in 3 energy label difference

  - 100.00% predictions in 4 energy label difference

  - 100.00% predictions in 5 energy label difference

These percentages indicate the accuracy of our API's predictions within specific error ranges. Higher percentages demonstrate the reliability and consistency of our API's performance

Measure Period: Q4 2023

Altum AI recently conducted performance testing on the NTA 8800 Energylabel API using a dataset of 1.679 addresses with the NTA 8800 determination method. The purpose of this testing was to evaluate the BENG2 accuracy of the NTA 8800 Energylabel API when handling address-related queries. The results of the testing are as follows:

• Mean Absolute Error: 20.59

• Median Absolute Error: 1.88

These metrics provide insights into the average and median deviation between predicted and actual BENG2 values. Lower values indicate higher accuracy and precision in our API's performance. In addition to these metrics, we have also analyzed the predictions within various error ranges:

- 79.09% predictions in 5 BENG2 units error range

- 82.13% predictions in 10 BENG2 units error range

- 86.90% predictions in 20 BENG2 units error range

- 90.47% predictions in 30 BENG2 units error range

- 92.32% predictions in 40 BENG2 units error range

- 93.03% predictions in 50 BENG2 units error range

- 93.69% predictions in 60 BENG2 units error range

These percentages indicate the accuracy of our API's predictions within specific error ranges. Higher percentages demonstrate the reliability and consistency of our API's performance.