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A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems.

¹National Renewable Energy Laboratory, Buildings and Thermal Sciences Center, Golden, CO 80401, USA.

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|October 24, 2020

Summary

This summary is machine-generated.

This study introduces a new framework for imputing missing building sensor data. The automated ensemble method improves data imputation accuracy by 18.2% on average.

Keywords:

building sensors data imputation ensemble method machine learning missing data pattern recognition

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Area of Science:

Building energy systems
Data quality management
Sensor networks

Background:

Building operation data are crucial for energy system monitoring and control.
Missing sensor data is a significant challenge impacting data quality and analysis.
Current imputation methods lack customization, automation, and robust validation.

Purpose of the Study:

To develop a customized and automated framework for missing sensor data imputation in buildings.
To address the challenge of validating data imputation method performance.
To improve the accuracy and reliability of building sensor data.

Main Methods:

A validation data generation module using pattern recognition was developed to create a benchmark dataset.
An ensemble method was employed, testing multiple imputation techniques to identify the optimal method per sensor.
The framework was tested on 18 sensors from a real campus building environment.

Main Results:

The developed framework successfully generated a validation dataset for quantifying imputation performance.
The ensemble method identified sensor-specific optimal imputation strategies, accounting for data characteristics.
An average accuracy improvement of 18.2% in data imputation was achieved compared to the best single imputation method.

Conclusions:

The proposed framework effectively addresses the gaps in automated and validated missing sensor data imputation.
The ensemble approach offers a superior, sensor-specific imputation strategy for building energy systems.
This work enhances the reliability of building operational data for improved energy management.