Short-Term Load Forecasting Based on a Hybrid Deep Learning Model

Short term load prediction plays a critical role in the planning and operations of electric power systems especially in the modern days with high emphasis on integration of renewable energy resources. In this work, a hybrid deep learning model for short term load forecasting (STLF) is presented. The proposed method first decomposes the time series data into several intrinsic mode functions (IMF) using Empirical Mode Decomposition (EMD) and a reconstruction of the original series is obtained by suppressing the irrelevant IMFs. Detrended fluctuation analysis (DFA) is applied to each IMF to determine their scaling exponents for robust denoising performance. The denoised data is then used as input to the Deep Belief Network (DBN) model for modeling and prediction. Real data which represents hourly load consumption from the Electric Reliability Council of Texas (ERCOT) was used to evaluate the efficacy of the proposed method.

[1]  Abdollah Homaifar,et al.  A deep learning based approach for long-term drought prediction , 2017, SoutheastCon 2017.

[2]  Jiahao Deng,et al.  Short-Term Power Load Forecasting with Deep Belief Network and Copula Models , 2017, 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC).

[3]  Ren Zhang,et al.  A model with Fuzzy Granulation and Deep Belief Networks for exchange rate forecasting , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[4]  Aydin Akan,et al.  Detrended fluctuation thresholding for empirical mode decomposition based denoising , 2014, Digit. Signal Process..

[5]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[6]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[7]  Kunikazu Kobayashi,et al.  Time series forecasting using a deep belief network with restricted Boltzmann machines , 2014, Neurocomputing.

[8]  Marwan Bikdash,et al.  Simulating the effect of soft undulating surface on outdoor noise propagation using equivalent sources , 2015, SoutheastCon 2015.

[9]  Bri-Mathias Hodge,et al.  Short-Term Load Forecast Error Distributions and Implications for Renewable Integration Studies , 2013, 2013 IEEE Green Technologies Conference (GreenTech).

[10]  Gerald K. Ijemaru,et al.  Electric Power Transmission Enhancement: A Case of Nigerian Electric Power Grid , 2016 .

[11]  Agostino Tarsitano,et al.  Short-term load forecasting using a two-stage sarimax model , 2017 .

[12]  Li Li,et al.  Application of Chaos and Neural Network in Power Load Forecasting , 2011 .

[13]  Yusuf Yaslan,et al.  Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting , 2017 .

[14]  Qiongji Jin,et al.  Design of Deep Belief Networks for Short-Term Prediction of Drought Index Using Data in the Huaihe River Basin , 2012 .

[15]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[16]  Christofer Toumazou,et al.  Prediction of exchange rates using averaging intrinsic mode function and multiclass support vector regression , 2013, Artif. Intell. Res..

[17]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[18]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[19]  Tao Liu,et al.  A Novel Text Classification Approach Based on Deep Belief Network , 2010, ICONIP.

[20]  Xiaochao Wang,et al.  A Four-Stage Hybrid Model for Hydrological Time Series Forecasting , 2014, PloS one.

[21]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[22]  Ljupco Kocarev,et al.  Deep belief network based electricity load forecasting: An analysis of Macedonian case , 2016 .

[23]  A. Boudraa,et al.  EMD-Based Signal Noise Reduction , 2005 .

[24]  Angelos K. Marnerides,et al.  Short term power load forecasting using Deep Neural Networks , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).

[25]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[26]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[27]  Norbert A. Agana,et al.  EMD-Based Predictive Deep Belief Network for Time Series Prediction: An Application to Drought Forecasting , 2018 .

[28]  R. Weron.  Estimating long range dependence: finite sample properties and confidence intervals , 2001, cond-mat/0103510.

[29]  Tijmen Tieleman,et al.  Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.

[30]  Marwan Bikdash,et al.  Modeling the effect of atmospheric attenuation on a propagation outdoor noise , 2016, SoutheastCon 2016.

[31]  Babak Hossein Khalaj,et al.  Grey Prediction Based Handoff Algorithm , 2007 .

[32]  Hongseok Kim,et al.  Deep neural network based demand side short term load forecasting , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[33]  Hongseok Kim,et al.  Deep Neural Network Based Demand Side Short Term Load Forecasting , 2016 .

[34]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[35]  Ajay Shekhar Pandey,et al.  Short-term load forecasting methods: A review , 2016, 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES).

[36]  Herui Cui,et al.  An Empirical Research on Short Term Power Load Forecasting Based on Chaos Theory , 2008, 2008 International Seminar on Future Information Technology and Management Engineering.

[37]  Marwan Bikdash,et al.  Line-graph based modeling for assessing the vulnerability of transmission lines , 2017, SoutheastCon 2017.

[38]  Gary L. Lebby,et al.  Synchrophasor for enhancing operational reliability of Nigerian electric power grid , 2017, SoutheastCon 2017.