Abstract
Hospitals are one of the most energy-consuming commercial buildings in many countries as a highly complex organization because of a continuous energy utilization and great variability of usage characteristic. With the development of machine learning techniques, it can offer opportunities for predicting the energy consumptions in hospital. With a case hospital building in Norway, through analyzing the characteristic of this building, this paper focused on the prediction of energy consumption through machine learning methods (ML), based on the historical weather data and monitored energy use data within the last four consecutive years. A deep framework of machine learning was proposed in six steps: including data collecting, preprocessing, splitting, fitting, optimizing and estimating. It results that, in Norwegian hospital, Electricity was the most highly demand in main building by consuming 55% of total energy use, higher than district heating and cooling. By means of optimizing the hyper-parameters, this paper selected the specific parameters of model to predict the electricity with high accuracy. It concludes that Random forest and AdaBoost method were much better than decision tree and bagging, especially in predicting the lower energy consumption.
Supported by the Norwegian University of Science and Technology (NTNU), the St. Olavs Hospital in Norway, the China Scholarship Council (CSC), the National Key R&D Program of China (No. 2018YFD1100704) and the Graduate Scientific Research and Innovation Foundation of Chongqing (No. CYB17006).
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References
Ürge-Vorsatz, D., Cabeza, L.F., Serrano, S., Barreneche, C., Petrichenko, K.: Heating and cooling energy trends and drivers in buildings. Renew. Sustain. Energy Rev. 41, 85–98 (2015)
Bagnasco, A., Fresi, F., Saviozzi, M., Silvestro, F., Vinci, A.: Electrical consumption forecasting in hospital facilities: an application case. Energy Building 103, 261–270 (2015)
González, A.G., Sanz-Calcedo, J., Salgado, D.: Evaluation of energy consumption in german oshpitals: benchmarking in the public sector. Energies 11, 2279 (2018)
Dobosi, I., Tanasa, C., Kaba, N.-E., Retezan, A., Mihaila, D.: Building energy modelling for the energy performance analysis of a hospital building in various locations. E3S Web of Conferences 111, p. 06073 (2019)
Rohde, T., Martinez, R.: Equipment and energy usage in a large teaching hospital in norway. J. Healthc. Eng. 6, 419–434 (2015)
Lindberg, K., Bakker, S., Sartori, I.: Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts. Utilities Policy 58, 63–88 (2019)
Chen, Y., Luh, P., Rourke, S.: Short-term load forecasting: similar day-based wavelet neural networks. IEEE Trans. Power Syst. 25(1), 322–330 (2008)
Yan, J., Tian, C., Huang, J., Wang, Y.: Load forecasting using twin gaussian process model. In: Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 36–41. IEEE (2012)
Yanxia, L., Shi, H.-F.: The hourly load forecasting based on linear Gaussian state space model. In: 2012 International Conference on Machine Learning and Cybernetics 2, pp. 741–747. IEEE (2012)
Khosravi, A., Nahavandi, S.: Load forecasting using interval type-2 fuzzy logic systems: optimal type reduction. IEEE Trans. Ind. Inform. 10(2), 1055–1063 (2013)
Jetcheva, J.G., Majidpour, M., Chen, W.-P.: Neural network model ensembles for building-level electricity load forecasts. Energy Build. 84, 214–223 (2014)
Richalet, V., Neirac, F.P., Tellez, F., Marco, J., Bloem, J.J.: HELP (house energy labeling procedure): methodology and present results. Energy Build. 33(3), 229–233 (2001)
Li, W., Gong, G., Fan, H., Peng, P., Chun, L.: Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting. Appl. Energy 270, 115144 (2020)
Liu, J.Y., Chen, H.X., Wang, J.Y., Li, G.N., Shi, S.B.: Time Series Prediction of the Indoor Temperature in the Subway Station Based on Data Mining Techniques. Kung Cheng Je Wu Li Hsueh Pao/Journal of Engineering Thermophysics 39(6), 1316–1321 (2018)
Sendra-Arranz, R., Gutiérrez, A.: A long short-term memory artificial neural network to predict daily HVAC consumption in buildings. Energy Building 216, 109952 (2020)
Liu, T., Xu, C., Guo, Y., Chen, H.: A novel deep reinforcement learning based methodology for short-term HVAC system energy consumption prediction. Int. J. Refrig 107, 39–51 (2019)
Huang, Y., Yuan, Y., Chen, H., Wang, J., Guo, Y., Ahmad, T.: A novel energy demand prediction strategy for residential buildings based on ensemble learning. Energy Procedia 158, 3411–3416 (2019)
Alamin, Y.I., Álvarez, J.D., del Mar Castilla, M., Ruano, A.: An Artificial Neural Network (ANN) model to predict the electric load profile for an HVAC system. IFAC-PapersOnLine 51(10), 26–31 (2018)
Yu, Z., Haghighat, F., Fung, B.C.M., Yoshino, H.: A decision tree method for building energy demand modeling. Energy Buildings 42(10), 1637–1646 (2010)
Gong, B., Ordieres-Meré, J.: Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong. Environ. Model Softw. 84, 290–303 (2016)
Breiman, L.: Machine Learning, Volume 45, Number 1 - SpringerLink. Mach. Learn. 45, 5–32 (2001)
Wu, Z., et al.: Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings. Energy Buildings 173, 117–127 (2018)
Ridgeway, G.: Generalized boosted models: a guide to the GBM package. Comput. 1, 1–12 (2005)
Scikit-learn. https://scikit-learn.org/. Accessed 2020
Directorate, T.B.Q.: https://dibk.no/byggereglene/byggteknisk-forskrift-tek17/10/innledning. Accessed 2020
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Xue, K. et al. (2020). A Simple and Novel Method to Predict the Hospital Energy Use Based on Machine Learning: A Case Study in Norway. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_2
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