Abstract
Electricity consumption forecasting is practically significant for either detecting abnormal power usage pattern or resource-conserving purpose. Indeed, it is a non-trivial task since electricity consumption is related to multiple complex factors, including historical amount of consumption, calendar dates and holidays, as well as residential power consumption habits. To this end, we propose an end-to-end structure to collectively forecast short-term power consumption of private households, called RCFNet (Residual Conventional Fusion Network). Specifically, our RCFNet uses (1) three branches of residual convolutional units to model the temporal proximity, periodicity and tendency properties of electricity consumption, (2) one fully connected neural network to model the weekday or weekend property, and (3) a residual convolution network to fuse the above output to produce short-term prediction. All the convolutions used here are one-dimensional. Through experimental studies on residential electricity consumption dataset in Australia, it is validated that the proposed RCFNet outperforms several well-known methods. Besides, we demonstrate that residential power consumption is closely related to the living characteristics of residents.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
W. Kong, Z.Y. Dong, Y. Jia et al., Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid PP(99), 1–1 (2017)
O.I. Asensio, M.A. Delmas, Nonprice incentives and energy conservation. Proc. Natl. Acad. Sci. 112(6), 510–515 (2015)
A. Marinescu, C. Harris, I. Dusparic et al., Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods, in International Workshop on Software Engineering Challenges for the Smart Grid (IEEE, 2013), pp. 25–32
E. Mocanu, P.H. Nguyen, M. Gibescu et al., Deep learning for estimating building energy consumption. Sustain. Energy Grids Netw. 6, 91–99 (2016)
A. Veit, C. Goebel et al., Household electricity demand forecasting: benchmarking state-of-the-art methods (2014)
B. Stephen, X. Tang, P.R. Harvey et al., Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting. IEEE Trans. Smart Grid 8(4), 1591–1598 (2017)
S. Humeau, T.K. Wijaya, M. Vasirani et al., Electricity load forecasting for residential customers: exploiting aggregation and correlation between households, in Sustainable Internet and ICT for Sustainability (IEEE, 2013), pp. 1–6
M.D. Wagy, J.C. Bongard, J.P. Bagrow et al., Crowdsourcing predictors of residential electric energy usage. IEEE Syst. J. PP(99), 1–10 (2017)
Y.H. Hsiao, Household electricity demand forecast based on context information and user daily schedule analysis from meter data. IEEE Trans. Industr. Inf. 11(1), 33–43 (2017)
Y. Wang, Q. Xia, C. Kang, Secondary forecasting based on deviation analysis for short-term load forecasting. IEEE Trans. Power Syst. 26(2), 500–507 (2011)
X. Cao, S. Dong, Z. Wu et al., A data-driven hybrid optimization model for short-term residential load forecasting, in IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (IEEE, 2015), pp. 283–287
Y. Li, D. Niu, Application of principal component regression analysis in power load forecasting for medium and long term, in International Conference on Advanced Computer Theory and Engineering (IEEE, 2010), pp. V3-201–V3-203
Z.H. Osman, M.L. Awad, T.K. Mahmoud, Neural network based approach for short-term load forecasting. Int. J. Sci. Environ. Technol. 1(5), 1–8 (2012)
N. Ye, Y. Liu, Y. Wang, Short-term power load forecasting based on SVM, in World Automation Congress (IEEE, 2012), pp. 47–51
C.L. Zhang, Power system short-term load forecasting based on fuzzy clustering analysis and rough sets. J. North China Electric Power Univ. (2008)
Q. Pang, M. Zhang, Very short-term load forecasting based on neural network and rough set, in International Conference on Intelligent Computation Technology and Automation (IEEE, 2010), pp. 1132–1135
M. Chaouch, Clustering-based improvement of nonparametric functional time series forecasting: application to intra-day household-level load curves. IEEE Trans. Smart Grid 5(1), 411–419 (2014)
M. Ghofrani, M. Hassanzadeh, M. Etezadi-Amoli et al., Smart meter based short-term load forecasting for residential customers, in North American Power Symposium (IEEE, 2011), pp. 1–5
S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
D.L. Marino, K. Amarasinghe, M. Manic, Building Energy Load Forecasting Using Deep Neural Networks (2016)
W. Kong, Z.Y. Dong, Y. Jia et al., Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid PP(99), 1–1 (2017)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9
K. He, X. Zhang, S. Ren et al., Deep Residual Learning for Image Recognition (2015), pp. 770–778
J. Zhang, Y. Zheng, D. Qi, Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2016)
K. He, X. Zhang, S. Ren et al., Identity mappings in deep residual networks, in European Conference on Computer Vision (Springer, Cham, 2016), pp. 630–645
Y. Lecun, L. Bottou, Y. Bengio et al., Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in Computer Vision and Pattern Recognition (IEEE, 2015), pp. 3431–3440
S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, in ICML, pp. 448–456
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization. Comput. Sci. (2014)
S. Ruder, An Overview of Gradient Descent Optimization Algorithms (2016)
J. Duchi, E. Hazan, Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
M.D. Zeiler, ADADELTA: an adaptive learning rate method. Comput. Sci. (2012)
G. Hinton, N. Srivastava, K. Swersky, RMSProp: divide the gradient by a running average of its recent magnitude, in Neural Networks for Machine Learning, Coursera lecture 6e (2012)
F. Chollet, Keras (2015). https://github.com/fchollet/keras
M. Abadi, A. Agarwal, P. Barham et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016)
Acknowledgements
This research work was partly supported by National Key Research and Development Program of China (Grant No. 2016YFC0800100), Major Research Program of the National Natural Science Foundation of China (Grant No. 91546103), and Anhui Provincial Natural Science Foundation (Grant No. 1708085QG162).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lei, M., Tang, L., Li, M., Ye, Z., Pan, L. (2019). Forecasting Short-Term Residential Electricity Consumption Using a Deep Fusion Model. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 529. Springer, Singapore. https://doi.org/10.1007/978-981-13-2291-4_36
Download citation
DOI: https://doi.org/10.1007/978-981-13-2291-4_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2290-7
Online ISBN: 978-981-13-2291-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)