A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Abdulaziz Almalaq, Jun Hao, Jun Jason Zhang and Fei-Yue Wang, "Parallel Building: A Complex System Approach for Smart Building Energy Management," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1452-1461, Nov. 2019. doi: 10.1109/JAS.2019.1911768
Citation: Abdulaziz Almalaq, Jun Hao, Jun Jason Zhang and Fei-Yue Wang, "Parallel Building: A Complex System Approach for Smart Building Energy Management," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1452-1461, Nov. 2019. doi: 10.1109/JAS.2019.1911768

Parallel Building: A Complex System Approach for Smart Building Energy Management

doi: 10.1109/JAS.2019.1911768
More Information
  • These days’ smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5G, the ACP theory (i.e., artificial systems, computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

     

  • loading
  • [1]
    K. Amarasinghe, D. L. Marino, and M. Manic, " Deep neural networks for energy load forecasting,” in Proc. 26th IEEE Int. Symposium on Industrial Electronics (ISIE), Jun. 2017, pp. 1483–1488.
    [2]
    K. Amarasinghe, D. Wijayasekara, H. Carey, M. Manic, D. He, and W. P. Chen, " Artificial neural networks based thermal energy storage control for buildings,” in Proc. IECON 41st Annual Conf. the IEEE Industrial Electronics Society, Nov. 2015, pp. 005421–005426.
    [3]
    L. Pérez-Lombard, J. Ortiz, and C. Pout, " A review on buildings energy consumption information,” Energy and Buildings, vol. 40, no. 3, pp. 394–398, 2008. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0378778807001016
    [4]
    F.-Y. Wang, " Toward a paradigm shift in social computing: the acp approach,” IEEE Intelligent Systems, vol. 22, no. 5, pp. 65–67, Sep. 2007. doi: 10.1109/MIS.2007.4338496
    [5]
    F.-Y. Wang, X. Wang, L. Li, and L. Li, " Steps toward parallel intelligence,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 4, pp. 345–348, Oct. 2016. doi: 10.1109/JAS.2016.7510067
    [6]
    J. J. Zhang, D. W. Gao, Y. Zhang, X. Wang, X. Zhao, D. Duan, X. Dai, J. Hao, and F.-Y. Wang, " Social energy: mining energy from the society,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 466–482, 2017. doi: 10.1109/JAS.2017.7510547
    [7]
    A. M. Khudhair and M. M. Farid, " A review on energy conservation in building applications with thermal storage by latent heat using phase change materials,” Energy Conversion and Management, vol. 45, no. 2, pp. 263–275, 2004. doi: 10.1016/S0196-8904(03)00131-6
    [8]
    E. Mocanu, P. H. Nguyen, M. Gibescu, and W. L. Kling, " Comparison of machine learning methods for estimating energy consumption in buildings,” in Proc. Probabilistic Methods Applied to Power Systems (PMAPS), Int. Conf. IEEE, 2014, pp. 1–6.
    [9]
    H.-X. Zhao and F. Magoulès, " A review on the prediction of building energy consumption,” Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 3586–3592, 2012. doi: 10.1016/j.rser.2012.02.049
    [10]
    N. Amjady, " Short-term hourly load forecasting using time-series modeling with peak load estimation capability,” IEEE Trans. Power Systems, vol. 16, no. 3, pp. 498–505, Aug. 2001. doi: 10.1109/59.932287
    [11]
    M. T. Hagan and S. M. Behr, " The time series approach to short term load forecasting,” IEEE Trans. Power Systems, vol. 2, no. 3, pp. 785–791, Aug. 1987. doi: 10.1109/TPWRS.1987.4335210
    [12]
    J. Contreras, R. Espinola, F. J. Nogales, and A. J. Conejo, " Arima models to predict next-day electricity prices,” IEEE Trans. Power Systems, vol. 18, no. 3, pp. 1014–1020, Aug. 2003. doi: 10.1109/TPWRS.2002.804943
    [13]
    J. Hao, X. Dai, Y. Zhang, J. Zhang, and W. Gao, " Distribution locational real-time pricing based smart building control and management,” in Proc. North American Power Symposium (NAPS), Sept. 2016, pp. 1–6.
    [14]
    S. L. Wong, K. K. Wan, and T. N. Lam, " Artificial neural networks for energy analysis of office buildings with daylighting,” Applied Energy, vol. 87, no. 2, pp. 551–557, 2010. doi: 10.1016/j.apenergy.2009.06.028
    [15]
    S. A. Kalogirou, " Artificial neural networks in energy applications in buildings,” International J. Low-Carbon Technologies, vol. 1, no. 3, pp. 201–216, 2006. doi: 10.1093/ijlct/1.3.201
    [16]
    C. Roldán-Blay, G. Escrivá-Escrivá, C. Álvarez-Bel, C. Roldán-Porta, and J. Rodríguez-García, " Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model,” Energy and Buildings, vol. 60, pp. 38–46, 2013. doi: 10.1016/j.enbuild.2012.12.009
    [17]
    J. G. Jetcheva, M. Majidpour, and W.-P. Chen, " Neural network model ensembles for building-level electricity load forecasts,” Energy and Buildings, vol. 84, pp. 214–223, 2014. doi: 10.1016/j.enbuild.2014.08.004
    [18]
    M. De Felice and X. Yao, " Short-term load forecasting with neural network ensembles: a comparative study [application notes],” IEEE Computational Intelligence Magazine, vol. 6, no. 3, pp. 47–56, 2011. doi: 10.1109/MCI.2011.941590
    [19]
    B. Dong, C. Cao, and S. E. Lee, " Applying support vector machines to predict building energy consumption in tropical region,” Energy and Buildings, vol. 37, no. 5, pp. 545–553, 2005. doi: 10.1016/j.enbuild.2004.09.009
    [20]
    Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida, " Applying support vector machine to predict hourly cooling load in the building,” Applied Energy, vol. 86, no. 10, pp. 2249–2256, 2009. doi: 10.1016/j.apenergy.2008.11.035
    [21]
    L. Ghelardoni, A. Ghio, and D. Anguita, " Energy load forecasting using empirical mode decomposition and support vector regression,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 549–556, 2013. doi: 10.1109/TSG.2012.2235089
    [22]
    B.-J. Chen, M.-W. Chang, and C.-J. Lin, " Load forecasting using support vector machines: a study on eunite competition 2001,” IEEE Trans. Power Systems, vol. 19, no. 4, pp. 1821–1830, 2004. doi: 10.1109/TPWRS.2004.835679
    [23]
    Y.-C. Li, T.-J. Fang, and E.-K. Yu, " Study of support vector machines for short-term load forecasting,” Proc. the CSEE, vol. 5, pp. 654–659, 2003.
    [24]
    Q. Ding, " Long-term load forecast using decision tree method,” in Proc. IEEE PES Power Systems Conf. and Exposition, Oct. 2006, pp. 1541–1543.
    [25]
    M. A. Al-Gunaid, M. V. Shcherbakov, D. A. Skorobogatchenko, A. G. Kravets, and V. A. Kamaev, " Forecasting energy consumption with the data reliability estimatimation in the management of hybrid energy system using fuzzy decision trees,” in Proc. 7th Int. Conf. Information, Intelligence, Systems Applications (ⅡSA), July 2016, pp. 1–8.
    [26]
    Y. Y. Chen, Y. S. Lv, Z. J. Li, and F.-Y. Wang, " Long short-term memory model for traffic congestion prediction with online open data,” in Proc. IEEE 19th Int. Conf. Intelligent Transportation Systems (ITSC), Nov. 2016, pp. 132–137.
    [27]
    R. Zhang, Y. Xu, Z. Y. Dong, W. Kong, and K. P. Wong, " A composite k-nearest neighbor model for day-ahead load forecasting with limited temperature forecasts,” in Proc. IEEE Power and Energy Society General Meeting (PESGM), Jul. 2016, pp. 1–5.
    [28]
    W. Kong, Z. Y. Dong, D. J. Hill, F. Luo, and Y. Xu, " Short-term residential load forecasting based on resident behaviour learning,” IEEE Trans. Power Systems, vol. 33, no. 1, pp. 1087–1088, 2018. doi: 10.1109/TPWRS.2017.2688178
    [29]
    H. Shi, M. Xu, and R. Li, " Deep learning for household load forecasting — a novel pooling deep rnn,” IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 5271–5280, Sept. 2018. doi: 10.1109/TSG.2017.2686012
    [30]
    F. M. Bianchi, E. Maiorino, and M. C. Kampffmeyer, A. Rizzi, and R. Jenssen, " An overview and comparative analysis of recurrent neural networks for short term load forecasting,” arXiv preprint arXiv: 1705.04378, 2017.
    [31]
    J. Zheng, C. Xu, Z. Zhang, and X. Li, " Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network,” in Proc. 51st Annual Conf. Information Sciences and Systems (CISS), Mar. 2017, pp. 1–6.
    [32]
    D. Gan, Y. Wang, N. Zhang, and W. Zhu, " Enhancing short-term probabilistic residential load forecasting with quantile long-short-term memory,” The J. Engineering, vol. 2017, no. 14, pp. 2622–2627, 2017. doi: 10.1049/joe.2017.0833
    [33]
    A. Almalaq and J. J. Zhang, " Evolutionary deep learning-based energy consumption prediction for buildings,” IEEE Access, vol. 7, pp. 1520–1531, 2019. doi: 10.1109/ACCESS.2018.2887023
    [34]
    D. L. Marino, K. Amarasinghe, and M. Manic, " Building energy load forecasting using deep neural networks,” in Proc. Industrial Electronics Society, IECON 42nd Annual Conf. of the IEEE. IEEE, 2016, pp. 7046–7051.
    [35]
    S. Kumar, L. Hussain, S. Banarjee, and M. Reza, " Energy load forecasting using deep learning approach-lstm and gru in spark cluster,” in Proc. 5th Int. Conf. Emerging Applications of Information Technology (EAIT), Jan. 2018, pp. 1–4.
    [36]
    K. Lu, Y. Zhao, X. Wang, Y. Cheng, X. K. Peng, W. X. Sun, et al., " Short-term electricity load forecasting method based on multilayered self-normalizing gru network,” in Proc. IEEE Conf. Energy Internet and Energy System Integration (EI2), Nov. 2017, pp. 1–5.
    [37]
    A. Almalaq and G. Edwards, " A review of deep learning methods applied on load forecasting,” in Proc. 16th IEEE Int. Conf. Machine Learning and Applications (ICMLA), Dec. 2017, pp. 511–516.
    [38]
    W. Kong, Z. Y. Dong, D. J. Hill, F. Luo, and Y. Xu, " Short-term residential load forecasting based on resident behaviour learning,” IEEE Trans. Power Systems, 2017.
    [39]
    X. Zhou, Q. D. Liu, G. Q. Liu, J. W. Yan, J. C. Yang, L. Q. Liang, et al., " Multi-variable time series forecasting for thermal load of air-conditioning system on svr,” in Proc. 34th Chinese Control Conf. (CCC), Jul. 2015, pp. 8276–8280.
    [40]
    N. Fumo and M. R. Biswas, " Regression analysis for prediction of residential energy consumption,” Renewable and Sustainable Energy Reviews, vol. 47, pp. 332–343, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364032115001884
    [41]
    F. H. Al-Qahtani and S. F. Crone, " Multivariate k-nearest neighbour regression for time series data — a novel algorithm for forecasting uk electricity demand,” in Proc. Int. Joint Conf. Neural Networks (IJCNN), Aug. 2013, pp. 1–8.
    [42]
    G. K. Tso and K. K. Yau, " Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks,” Energy, vol. 32, no. 9, pp. 1761–1768, 2007. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0360544206003288
    [43]
    Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu, " Recurrent neural networks for multivariate time series with missing values,” Scientific Reports, vol. 8, no. 1, pp. 6085, 2018. doi: 10.1038/s41598-018-24271-9
    [44]
    I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org.
    [45]
    G. Zhang, B. E. Patuwo, and M. Y. Hu, " Forecasting with artificial neural networks: the state of the art,” Int. J. Forecasting, vol. 14, no. 1, pp. 35–62, 1998. doi: 10.1016/S0169-2070(97)00044-7
    [46]
    H. S. Hippert, C. E. Pedreira, and R. C. Souza, " Neural networks for short-term load forecasting: a review and evaluation,” IEEE Trans. Power Systems, vol. 16, no. 1, pp. 44–55, 2001. doi: 10.1109/59.910780
    [47]
    H. K. Alfares and M. Nazeeruddin, " Electric load forecasting: literature survey and classification of methods,” Int. J. Systems Science, vol. 33, no. 1, pp. 23–34, 2002. doi: 10.1080/00207720110067421
    [48]
    A. Almalaq and G. Edwards, " Comparison of recursive and nonrecursive anns in energy consumption forecasting in buildings,” in Proc. IEEE Green Technologies Conf. (GreenTech), pp. 1–5, Apr. 2019.
    [49]
    I. Sutskever, O. Vinyals, and Q. V. Le, " Sequence to sequence learning with neural networks,” in Proc. Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 3104–3112. [Online]. Available: http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
    [50]
    S. Ryu, J. Noh, and H. Kim, " Deep neural network based demand side short term load forecasting,” Energies, vol. 10, no. 1, pp. 3, 2016. doi: 10.3390/en10010003
    [51]
    Y. LeCun, Y. Bengio, and G. Hinton, " Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. doi: 10.1038/nature14539
    [52]
    F. Chollet et al., " Keras,” [Online]. Available: https://github.com/fchollet/keras, 2015.
    [53]
    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, " Scikit-learn: machine learning in Python,” J. Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(4)

    Article Metrics

    Article views (1651) PDF downloads(49) Cited by()

    Highlights

    • The ACP theory enhanced the modeling of complex systems such as buildings.
    • Multivariate time series (MTS) problem can be modeled with sequential deep learning (DL) methods.
    • The objective is to model a DL energy consumption prediction using MTS.
    • The MTS models improved the prediction accuracy in complex systems such as buildings.
    • The proposed framework can be applied to many other smart environment problems.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return