Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings
Abstract
:1. Introduction
2. Presentation of the Dataset and the Studied Building
- 7 sensors for temperature and humidity;
- 13 other sensors for temperature, humidity, and CO2 on each;
- 3 sensors for the opening and closing of 2 windows and the break room door;
- 3 sensors for the use of meeting rooms by quantifying motion detected over time and ambient light, which are not used and not shared;
- 3 sensors for the concentrations in total volatile organic components (TVOCs) and particulate matter (PM1.0, PM2.5, PM10);
- 5 sensors for 5 HVAC on the power meter.
- 15 weeks of data to train in temperature forecasting;
- 6 weeks to evaluate temperature forecasting performances;
- 8 weeks of data to train in HVAC energy consumption forecasting;
- 6 weeks to evaluate HVAC energy consumption forecasting performances.
3. Related Work
4. Forecasting Neural Network Evaluation
4.1. Pragmatic Metric to Evaluate AI Performances
4.2. Forecasting Neural Network Complexity
4.3. Forecasting Policies
- Single feature monovariate time series processed by a single LSTM with the output concatenated with the calendar data;
- Multivariate time series processed by a single LSTM with the output concatenated with the calendar data;
- Monovariate time series processed by several LSTMs, with all the output concatenated with the calendar data.
5. Data Relevance
5.1. Feature Selection
5.2. History Length for Hourly Forecasting
5.3. Training Data Length Study
6. Findings
6.1. Synthesis on the Methodology
6.2. Synthesis on Data Relevance
7. Conclusions and Future Works
8. Legal Disclaimer
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. How to Play with Our Notebook?
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Closson, L.; Cérin, C.; Donsez, D.; Baudouin, J.-L. Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings. Information 2024, 15, 94. https://doi.org/10.3390/info15020094
Closson L, Cérin C, Donsez D, Baudouin J-L. Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings. Information. 2024; 15(2):94. https://doi.org/10.3390/info15020094
Chicago/Turabian StyleClosson, Louis, Christophe Cérin, Didier Donsez, and Jean-Luc Baudouin. 2024. "Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings" Information 15, no. 2: 94. https://doi.org/10.3390/info15020094