Medium-term Load Forecasting Method with Improved Deep Belief Network for Renewable Energy

  • Yan Liang Internet Business Department, State Grid Gansu Electric Power Company, Lanzhou, China
  • Li Zhi Anhui Jiyuan Software Co., Ltd., Hefei, China
  • Yu Haiwei Hefei Maxtech Information Technology Co., Ltd., Hefei, China
Keywords: IDBN-NN, renewable energy, power system, load forecasting, restricted Boltzmann machine, deep belief network

Abstract

With the continuous transition of the traditional power system to the new power system, the composition of the power generation side in the power system has gradually begun to be dominated by renewable energy (at least more than 50%). Among the renewable energy sources, wind power is the most susceptible to weather and environmental influences. These factors increase the complexity of the power generation mode, and put forward higher requirements for the accuracy and stability of load forecasting. This paper proposes a medium-term renewable energy load forecasting method based on an improved deep belief network (IDBN-NN). The method includes the construction of a deep belief network, the layer-by-layer pre-training and fine-tuning of model parameters, and the application of the model. In the process of model parameter pre-training, Gauss-Bernoulli Restricted Boltzmann Machine (GB-RBM) is used as the first part of the stacked deep belief network, so that it can process multiple types of real-valued input data more effectively. In addition, IDBN-NN uses a combination of unsupervised training and supervised training for pre-training. Finally, the actual load data is used to analyze the calculation example. When the number of RBM layers is 3, the number of fully connected layers is 1, and Dropout is equal to 0.2, the MSE and loss values are optimal, which are 0.0037 and 0.0104, respectively. The experimental results show that the proposed method has higher prediction accuracy when the training sample is large and the load influencing factors are complex.

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Author Biographies

Yan Liang, Internet Business Department, State Grid Gansu Electric Power Company, Lanzhou, China

Yan Liang (1986–), male, Han nationality, from Suide, Shaanxi, graduate degree, engineer, working in the Internet Business Department of State Grid Gansu Electric Power Company, engaged in power data management and data operation.

Li Zhi, Anhui Jiyuan Software Co., Ltd., Hefei, China

Li Zhi (1985–), male, Han nationality, native of Jingxian County, Anhui Province, bachelor degree, engineer, worked in the Enterprise Management Application Division of Anhui Jiyuan Software Co., Ltd. He has long been engaged in the construction of electric power information.

Yu Haiwei, Hefei Maxtech Information Technology Co., Ltd., Hefei, China

Yu Haiwei (1991–), male, Han nationality, native of Zibo, Shandong, bachelor degree, data analyst, working in the big data department of Hefei Maisitaihe Information Technology Co., Ltd., engaged in big data analysis and operation.

References

C. Q. Kang, Q. Xia, M. Liu. Power system load forecasting. Automation of Electric Power Systems, 2007, 6(16):457–467.

T. Hong, P. Wang, Willis H. L. A naive multiple linear regression benchmark for short term load forecasting [C]// IEEE Power and Energy Society General Meeting, July 24–29, 2011, Detroit, USA: 1–6.

Q. H. Wu, J. Jun, G. S. Hou, B. Han, K. Y. Wang. Online Recognition of Human Actions Based on Temporal Deep Belief Neural Network. Power system automation, 2016, 40(15):67–72.

L. Hernandez, C. Baladron, J. M. Aguiar, et al. Artificial neural network for short-term load forecasting in distribution systems[J]. Energies, 2014, 7(3):1576–1598.

J. Liu, H. Gao, M. A. Zhao. Review and prospect of active distribution system planning[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(4):457–467.

T. Y. Zhou, J. Q. Yi, Y. Yang, H. T. Zhang, X. F. Yuan. Online Recognition of Human Actions Based on Temporal Deep Belief Neural Network. Acta Automatica Sinica, 2016, 15(7):1030–1039.

Z. Q. Geng, Y. K. Zhang. An Improved Deep Belief Network Inspired by Glia Chains. Acta Automatica Sinica, 2016, 4(6):943–952.

G. E. Hinton, S. Osindero, Y. W. Teh. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527–1554.

W. Liu, Z. Wang, X. Liu. A survey of deep neural network architectures and their applications[J]. Neurocomputing, 2016, 234:11–26.

Z. H. Rong, B. Qi, C. R. Li, S. J. Zhu, Y. F. Chen. Combined DBN Diagnosis Method for Dissolved Gas Analysis of Power Transformer Oil. Power System Technology, 2019, 23(10):3800–3808.

J. Q. Shi, T. Tan, J. Guo, Y. Liu, J. H. Zhang. Multi-Task Learning Based on Deep Architecture for Various Types of Load Forecasting in Regional Energy System Integration. Power System Technology, 2018, 5(3):698–707.

X. B. Zhang, J. Tang, C. Pan, X. X. Zhang, M. Jin. Research of Partial Discharge Recognition Based on Deep Belief Nets. Power System Technology, 2016, 40(10):3283–3289.

T. Kuremoto, S. Kimura, K. Kobayashi, et al. Time series forecasting using a deep belief network with restricted Boltzmann machines[J]. Neurocomputing, 2014, 137(15):47–56.

X. Qiu, L. Zhang, Y. Ren, et al. Ensemble deeplearning for regression and time series forecasting[C]// IEEE Symposium on Computational Intelligence in Ensemble Learning, December 9–12, 2014, Orlando, USA: 1–6.

Dedineca, S. Filiposka, A. Dedinec, et al. Deep belief network based electricity load forecasting: an analysis of Macedonian case[J]. Energy, 2016, 115:1688–1700.

G. E. Hinton. A practical guide to training restricted Boltzmann machines[J]. Momentum, 2012, 9(1):599–619.

X. Y. Kong, F. Zheng, Z. J. E, J. Cao, X. Wang. Short-term Load Forecasting Based on Deep Belief Network. Power System Technology, 2018, 8(5):133–139.

J. X. Zhao, X. Zhang, F. Q. Di, S. S. Guo, X. Y. Li. Exploring the Optimum Proactive Defense Strategy for the Power Systems from an Attack Perspective. Security and Communication Networks, 2021(1): 1–14.

G. E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504–507.

Jinxiong Zhao, Sensen Guo, Dejun Mu. DouBiGRU-A: Software Defect Detection Algorithm Based on Attention Mechanism and Double BiGRU, Computers & Security, 2021, 13(5786):504–517.

You Weijing, Liu Limin, Ma Yue, et al. An Intel SGX-based Proof of Encryption in Clouds. Netinfo Security, 2020, 20(12):1–8.

Xu Guotian, Shen Yaotong. A Malware Detection Method Based on XGBoost and LightGBM Two-layer Model[J]. Netinfo Security, 2020, 20(12):54–63.

Li Hongjiao, Chen Hongyan. Research on Mobile Malicious Adversarial Sample Generation Based on WGAN[J]. Netinfo Security, 2020, 20(11):51–58.

Guo Qiquan, Zhang Haixia. Technology System for Security Protection of Critical Information Infrastructures[J]. Netinfo Security, 2020, 20(11):1–9.

Published
2021-12-08
Section
Renewable Power and Energy Systems