Air Handling Units Power Consumption Using Generalized Additive Model for Anomaly Detection: A Case Study in a Singapore Campus

Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

Abstract:

The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: Anomaly detection, digital twin, Generalised Additive Model, Power Consumption Model.

References:

[1] J. Luther and T. Reindl, Solar Photovoltaic (PV) Roadmap for Singapore (A Summary). Singapore: Solar Energy Research Institute of Singapore, 2013, pp. 29-31
[2] C.P. Au-Yong, A.S. Ali, F. Ahmad, Improving occupants’ satisfaction with effective maintenance management of HVAC system in office buildings, Autom. Constr. 43 (2014) 31–37.
[3] L. Perez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy Build. 40 (2008) 394–398.
[4] S. Wu, K. Neale, M. Williamson, M. Hornby, Research opportunities in maintenance of office building services systems, J. Qual. Maint. Eng. 16 (1) (2010) 23–33.
[5] National Environmental Agency, Energy Efficiency Opportunities Assessment and Energy Performance Measurement Guidelines for New Ventures (2018)
[6] J.-S. Chou and D.-K. Bui, “Modelling heating and cooling loads by artificial intelligence for energy-efficient building design,” Energy and Buildings, vol. 82, pp. 437–446, 2014.
[7] A. I. Dounis, “Artificial intelligence for energy conservation in buildings,” Advances in Building Energy Research, vol. 4, no. 1, pp. 267–299, 2010.
[8] M. Kim, S. Park, J. Lee, Y. Joo, and J. Choi, “Learning-based adaptive imputation method with KNN algorithm for missing power data,” Energies, vol. 10, no. 10, 2017.
[9] A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ann and svm for building electrical energy consumption forecasting,” Renewable
[10] T. Hastie and R. Tibshirani, Generalized Additive Models. Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Taylor & Francis, 1990.
[11] K. K. Sumer, O. Goktas, and A. Hepsag, “The application of seasonal latent variable in forecasting electricity demand as an alternative method,” Energy policy, vol. 37, no. 4, pp. 1317–1322, 2009.
[12] J. Ploennigs, B. Chen, A. Schumann, and N. Brady, “Exploiting generalized additive models for diagnosing abnormal energy use in buildings,” in Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, pp. 1–8, ACM, 2013.
[13] B. Chen, M. Sinn, J. Ploennigs, and A. Schumann, “Statistical anomaly detection in mean and variation of energy consumption,” in Pattern Recognition (ICPR), 2014 22nd International Conference on, pp. 3570– 3575, IEEE, 2014.
[14] S. J. Taylor and B. Letham, “Forecasting at Scale,” The American Statistician, vol. 72 (1), pp. 37-45, 2017.