Long short term memory networks for short-term electric load forecasting
Pages 2573 - 2578
Abstract
Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time. Demand patterns are extremely complex due to market deregulation and other environmental factors. Although there has been extensive research in the area of short-term electrical load forecasting, difficulties in implementation and lack of transparency in results has been cited as a main challenge. Deep neural architectures have recently shown their ability to mine complex underlying patterns in various domains. In our work, we present a deep recurrent neural architecture to unearth the complex patterns underlying the regional demand profiles without specific insights from the utilities. The model learns from historical data patterns. We show that deep recurrent neural network with long-short term memory architecture presents a robust methodology for accurate short term load forecasting with the ability to adapt and learn the underlying complex features over time. In most cases it matches the performance of the latest state-of-the-art techniques and even supercedes it in a few cases.
References
[1]
P. Geurts, “Pattern extraction for time series classification”, in European Conference on Principles of Data Mining and Knowledge Discovery. Springer, 2001, pp. 115–127.
[2]
A. Mardani, A. Jusoh, and E. K. Zavadskas, “Fuzzy multiple criteria decision-making techniques and applications-two decades review from 1994 to 2014”, Expert Systems with Applications, vol. 42, no. 8, pp. 4126–4148, 2015.
[3]
S. I. Vagropoulos, E. G. Kardakos, C. K. Simoglou, A. G. Bakirtzis, and J. P. Catalão, “Artificial neural network-based methodology for short-term electric load scenario generation”, in Intelligent System Application to Power Systems (ISAP), 2015 18th International Conference on. IEEE, 2015, pp. 1–6.
[4]
A. Narayan, K. W. Hipel, K. Ponnambalam, and S. Paul, “Neuro-fuzzy inference system (asupfunis) model for intervention time series prediction of electricity prices”, in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on. IEEE, 2011, pp. 2121–2126.
[5]
S. Li, P. Wang, and L. Goel, “Short-term load forecasting by wavelet transform and evolutionary extreme learning machine”, Electric Power Systems Research, vol. 122, pp. 96–103, 2015.
[6]
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning”, Nature, vol. 521, no. 7553, pp. 436–444, 2015.
[7]
U. Mukherjee, A. Maroufmashat, A. Narayan, A. Elkamel, and M. Fowler, “A stochastic programming approach for the planning and operation of a power to gas energy hub with multiple energy recovery pathways”, Energies, vol. 10, no. 7, p. 868, 2017.
[8]
A. Narayana and K. Ponnambalam, “Risk-averse stochastic programming approach for microgrid planning under uncertainty”, Renewable Energy, vol. 101, pp. 399–408, 2017.
[9]
G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
[10]
S.-J. Huang and K.-R. Shih, “Short-term load forecasting via arma model identification including non-gaussian process considerations”, IEEE Transactions on Power Systems, vol. 18, no. 2, pp. 673–679, 2003.
[11]
H. Hahn, S. Meyer-Nieberg, and S. Pickl, “Electric load forecasting methods: Tools for decision making”, European Journal of Operational Research, vol. 199, no. 3, pp. 902–907, 2009.
[12]
Z. Aung, M. Toukhy, J. Williams, A. Sanchez, and S. Herrero, “Towards accurate electricity load forecasting in smart grids”, in The Fourth International Conference on Advances in Databases, Knowledge, and Data Applications, DBKDA, 2012.
[13]
E. Ceperic, V. Ceperic, and A. Baric, “A strategy for short-term load forecasting by support vector regression machines”, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4356–4364, 2013.
[14]
C. Cecati, J. Kolbusz, P. Różycki, P. Siano, and B. M. Wilamowski, “A novel rbf training algorithm for short-term electric load forecasting and comparative studies”, IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6519–6529, 2015.
[15]
G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets”, Neural computation, vol. 18, no. 7, pp. 1527–1554, 2006.
[16]
T. Kuremoto, S. Kimura, K. Kobayashi, and M. Obayashi, “Time series forecasting using restricted boltzmann machine”, in International Conference on Intelligent Computing. Springer, 2012, pp. 17–22.
[17]
E. Busseti, I. Osband, and S. Wong, “Deep learning for time series modeling”, Technical report, Stanford University, Tech. Rep., 2012.
[18]
N. K. Ahmed, A. F. Atiya, N. E. Gayar, and H. El-Shishiny, “An empirical comparison of machine learning models for time series forecasting”, Econometric Reviews, vol. 29, no. 5–6, pp. 594–621, 2010.
[19]
G. Bontempi, S. B. Taieb, and Y.-A. Le Borgne, “Machine learning strategies for time series forecasting”, in Business Intelligence. Springer, 2013, pp. 62–77.
[20]
L. Beriman, “Bias, variance, and arching classifiers”, Technical Report, Tech. Rep., 1996.
[21]
S. Chatterjee, A. Dash, and S. Bandopadhyay, “Ensemble support vector machine algorithm for reliability estimation of a mining machine”, Quality and Reliability Engineering International, vol. 31, no. 8, pp. 1503–1516, 2015.
[22]
S. Hochreiter and J. Schmidhuber, “Long short-term memory”, Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[23]
Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult”, IEEE transactions on neural networks, vol. 5, no. 2, pp. 157–166, 1994.
[24]
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey”, arXiv preprint arXiv:, 2015.
[25]
IESO, “Independent electricity system operator”, http://www.ieso.ca/Pages/Power-Data/Demand.aspx
[26]
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization”, CoRR, vol. abs/1412.6980, 2014. [Online]. Available: http://arxiv.org/abs/1412.6980
Recommendations
Neural networks for pattern-based short-term load forecasting
In this work several univariate approaches for short-term load forecasting based on neural networks are proposed and compared. They include: multilayer perceptron, radial basis function neural network, generalized regression neural network, fuzzy ...
Conventional regression versus artificial neural network in short-term load forecasting
SpringSim '10: Proceedings of the 2010 Spring Simulation MulticonferenceIn order to short-term load forecasting (STLF), two different seasonal artificial neural networks (ANNs) are designed and compared with conventional regression. Furthermore designed ANNs are compared with each other in terms of model complexity, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Oct 2017
3827 pages
Copyright © 2017.
Publisher
IEEE Press
Publication History
Published: 05 October 2017
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 23 Dec 2024