Skip to content

ThoreWietzke/occupancy-benchmark-dataset

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

This repository contains the occupancy data from the paper "Occupancy Prediction for Building Energy Systems with Latent Force Models". They were collected at the Bosch Research Campus in Renningen, Germany. Additionally, an implementation of the Latent Force Model (LFM) predictor is provided

Running the example

The LFM is provided by the utilities package inside this repository. Install the package with

pip install .

and then run compare_prediction.py. This reproduces the prediction results from the paper in section 4.5.

Dataset

The occupancy dataset is found in the data folder inside the file Occupancy.mat. The basic metadata for the whole dataset is found in the table below.

Specification Field
Start time 01-Jan-2019
End time 15-Oct-2022
Sample time 15 minutes
Number of Zones 9

Zone Descripton

Zone layout of the building at Bosch

The dataset consists out of every zone in the layout above. Their index and description is found in the table below

Zone Index Description
9 0 Meeting Room
10 1 Meeting Room
11 2 Meeting Room
12 3 Kitchen
13 4 Copy Room
21 5 Open Space Office
22 6 Open Space Office
23 7 Open Space Office
24 8 Open Space Office

License

Only the occupancy dataset is distributed under the CC-BY-NC-SA License. If using or distributing the data, please cite the following paper (preprint):

@misc{wietzke2024occupancy,
    title = {Occupancy prediction for building energy systems with latent force models},
    journal = {Energy and Buildings},
    volume = {307},
    pages = {113968},
    year = {2024},
    issn = {0378-7788},
    doi = {https://doi.org/10.1016/j.enbuild.2024.113968},
    url = {https://www.sciencedirect.com/science/article/pii/S0378778824000847},
    author = {Thore Wietzke and Jan Gall and Knut Graichen},
    abstract = {This paper presents a new approach to predict the occupancy for building energy systems (BES). A Gaussian Process (GP) is used to model the occupancy and is represented as a state space model that is equivalent to the full GP if Kalman filtering and smoothing is used. The combination of GPs and mechanistic models is called Latent Force Model (LFM). An LFM-based model predictive control (MPC) concept for BES is presented that benefits from the extrapolation capability of mechanistic models and the learning ability of GPs to predict the occupancy within the building. Simulations with EnergyPlus and a comparison with real-world data from the Bosch Research Campus in Renningen show that a reduced energy demand and thermal discomfort can be obtained with the LFM-based MPC scheme by accounting for the predicted stochastic occupancy.}
}

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages