Preparing Weather Data for Real-Time Building Energy Simulation

TL;DR

This paper presents a neural network-based framework for real-time quality control of weather data, improving anomaly detection and missing data imputation to enhance building energy simulation accuracy.

Contribution

It introduces a novel neural network approach for weather data imputation, outperforming traditional methods especially with extended missing data periods.

Findings

Neural networks improve imputation accuracy over traditional methods.

The framework enables real-time quality control for building simulations.

Validated with weather data from Milan, Italy.

Abstract

This study introduces a framework for quality control of measured weather data, including anomaly detection, and infilling missing values. Weather data is a fundamental input to building performance simulations, in which anomalous values defect the results while missing data lead to an unexpected termination of the simulation process. Traditionally, infilling missing values in weather data is performed through periodic or linear interpolations. However, when missing values exceed many consecutive hours, the accuracy of traditional methods is subject to debate. This study demonstrates how Neural Networks can increase the accuracy of data imputation when compared to other supervised learning methods. The framework is validated by predicting missing temperature and relative humidity data for an observation site, through a network of nearby weather stations in Milan, Italy. Results show that the proposed method can facilitate real-time building simulations with accurate and rapid quality control.