IEEE/CAA Journal of Automatica Sinica
Citation:  Abdulaziz Almalaq, Jun Hao, Jun Jason Zhang and FeiYue Wang, "Parallel Building: A Complex System Approach for Smart Building Energy Management," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 14521461, Nov. 2019. doi: 10.1109/JAS.2019.1911768 
[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. PérezLombard, 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/S01968904(03)001316

[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, " Shortterm hourly load forecasting using timeseries 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 nextday 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 realtime 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. LowCarbon Technologies, vol. 1, no. 3, pp. 201–216, 2006. doi: 10.1093/ijlct/1.3.201

[16] 
C. RoldánBlay, G. EscriváEscrivá, C. ÁlvarezBel, C. RoldánPorta, and J. RodríguezGarcía, " 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 buildinglevel 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, " Shortterm 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 shortterm load forecasting,” Proc. the CSEE, vol. 5, pp. 654–659, 2003.

[24] 
Q. Ding, " Longterm load forecast using decision tree method,” in Proc. IEEE PES Power Systems Conf. and Exposition, Oct. 2006, pp. 1541–1543.

[25] 
M. A. AlGunaid, 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 shortterm 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 knearest neighbor model for dayahead 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, " Shortterm 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 longshorttermmemory 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 shortterm probabilistic residential load forecasting with quantile longshortterm 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 learningbased 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 approachlstm 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., " Shortterm electricity load forecasting method based on multilayered selfnormalizing 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, " Shortterm 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., " Multivariable time series forecasting for thermal load of airconditioning 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. AlQahtani and S. F. Crone, " Multivariate knearest 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/s41598018242719

[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/S01692070(97)000447

[46] 
H. S. Hippert, C. E. Pedreira, and R. C. Souza, " Neural networks for shortterm 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/5346sequencetosequencelearningwithneuralnetworks.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, " Scikitlearn: machine learning in Python,” J. Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
