Abstract
Energy consumption of heating, cooling, ventilation, lighting, and appliances is deeply influenced by human presence in buildings. Accurate room occupancy prediction is a key to making buildings cognitive and self-adapting in order to achieve energy efficiency and wastage cut. Instead of using cameras or human tracking devices, a predictive model based on indoor non-intrusive environmental sensors allows mitigating privacy concerns. In such direction, this study aims to develop a data-driven model for occupancy prediction using machine learning techniques based on a combination of temperature, humidity, CO2 concentration, light, and motion sensors. The approach has been designed and realized in a real scenario by leveraging the COGITO platform. The experimental results show that the proposed long short-term memory neural network is well suited to account for occupancy detection at the current state and occupancy prediction at the future state, respectively, with an overall detection rate of 99.5% and 92.6% on a literature dataset and 99.6% and 94.2% on a real scenario. These outcomes indicate the ability of the proposed model to monitor the occupancy information of spaces both in a real-time and in a short-term way.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Candanedo, L.M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and co2 measurements using statistical learning models. Energy Build. 112, 28–39 (2016)
Cao, X., Dai, X., Liu, J.: Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy Build. 128, 198–213 (2016)
Chen, Z., Masood, M.K., Soh, Y.C.: A fusion framework for occupancy estimation in office buildings based on environmental sensor data. Energy Build. 133, 790–798 (2016)
Chen, Z., Zhao, R., Zhu, Q., Masood, M.K., Soh, Y.C., Mao, K.: Building occupancy estimation with environmental sensors via cdblstm. IEEE Trans. Ind. Electron. 64(12), 9549–9559 (2017)
Das, S., Swetapadma, A., Panigrahi, C.: Building occupancy detection using feed forward back-propagation neural networks. In: 2017 3rd International Conference on Computational Intelligence and Networks (CINE), pp. 63–67. IEEE (2017)
Delzendeh, E., Wu, S., Lee, A., Zhou, Y.: The impact of occupants’ behaviours on building energy analysis: A research review. Renew. Sustain. Energy Rev. 80, 1061–1071 (2017)
Dong, B., Prakash, V., Feng, F., O’Neill, Z.: A review of smart building sensing system for better indoor environment control. Energy Build. 199, 29–46 (2019)
Erickson, V.L., Carreira-Perpiñán, M.Á., Cerpa, A.E.: Observe: Occupancy-based system for efficient reduction of hvac energy. In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 258–269. IEEE (2011)
Erickson, V.L., Carreira-Perpinán, M.A., Cerpa, A.E.: Occupancy modeling and prediction for building energy management. ACM Trans. Sensor Netw. (TOSN) 10(3), 1–28 (2014)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997)
Kim, S., Kang, S., Ryu, K.R., Song, G.: Real-time occupancy prediction in a large exhibition hall using deep learning approach. Energy Build. 199, 216–222 (2019)
Kleiminger, W., Beckel, C., Staake, T., Santini, S.: Occupancy detection from electricity consumption data. In: Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, pp. 1–8 (2013)
Levesque, A., Pietzcker, R.C., Luderer, G.: Halving energy demand from buildings: The impact of low consumption practices. Technol. Forecast. Soc. Change 146, 253–266 (2019)
Molina-Solana, M., Ros, M., Ruiz, M.D., Gómez-Romero, J., Martín-Bautista, M.J.: Data science for building energy management: A review. Renew. Sustain. Energy Rev. 70, 598–609 (2017)
Peng, Y., Rysanek, A., Nagy, Z., Schlüter, A.: Using machine learning techniques for occupancy prediction-based cooling control in office buildings. Applied Energy 211, 1343–1358 (2018)
Razavi, R., Gharipour, A., Fleury, M., Akpan, I.J.: Occupancy detection of residential buildings using smart meter data: A large-scale study. Energy Build. 183, 195–208 (2019)
Savaglio, C., Ganzha, M., Paprzycki, M., Bădică, C., Ivanović, M., Fortino, G.: Agent-based internet of things: State-of-the-art and research challenges. Futur. Gener. Comput. Syst. 102, 1038–1053 (2020)
Wang, W., Chen, J., Hong, T.: Occupancy prediction through machine learning and data fusion of environmental sensing and wi-fi sensing in buildings. Autom. Constr. 94, 233–243 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Colace, S., Laurita, S., Spezzano, G., Vinci, A. (2023). Room Occupancy Prediction Leveraging LSTM: An Approach for Cognitive and Self-Adapting Buildings. In: Cicirelli, F., Guerrieri, A., Vinci, A., Spezzano, G. (eds) IoT Edge Solutions for Cognitive Buildings. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-15160-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-15160-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15159-0
Online ISBN: 978-3-031-15160-6
eBook Packages: Computer ScienceComputer Science (R0)