A Survey of Electric Load Forecasting Algorithm Models

Qiushi Cao

doi:10.54097/hset.v9i.1881

Authors

Qiushi Cao

DOI:

https://doi.org/10.54097/hset.v9i.1881

Keywords:

ARIMA; Deep Learning; CNN; LSTM; Residual Network; Ensemble Network.

Abstract

With the increasing economic and social development, the load forecasting of the power system plays an increasingly important role in many energy-related fields such as energy dispatching, market-based price forecasting, and capacity allocation for the government and power companies. Based on the long-term research of many scholars, this paper aims to provide a general overview of some specific categories of load forecasting methods and models. Linear models can meet the real needs of forecasting in the early days, represented by autoregressive moving average (ARIMA) , but the lack of modeling ability requires researchers to develop more novel models based on machine learning and deep learning. Algorithms are widely used in the field of load forecasting, including human neural network (ANN) , support vector machine (SVM) , various variants of convolutional neural network (CNN) , memory network (RNN , LSTM...) , etc., which enhance the prediction performance and each have their own unique advantages. While the depth of the network continues to deepen, residual networks are proposed to optimize the model and solve a series of intractable problems. Different models are integrated and integrated together to form a new integrated network to give full play to the advantages of each model. However, these models do not solve the problem once and for all and have their own limitations. Finally, three different experiments are used to compare the three types of models mentioned in this article, and some typical network models are used as examples for performance analysis and demonstration. This review paper summarizes the valuable information of the prediction network, provides valuable information for subsequent research, and provides perspectives and entry points for subsequent research work.

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References

Juberias G, Yunta R, Moreno J G, et al. A new ARIMA model for hourly load forecasting[C]//1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333). IEEE, 1999, 1: 314-319.

Amjady N. Short-term hourly load forecasting using time-series modeling with peak load estimation capability[J]. IEEE Transactions on power systems, 2001, 16(3): 498-505.

Li Y, Han D, Yan Z. Long-term system load forecasting based on data-driven linear clustering method[J]. Journal of Modern Power Systems and Clean Energy, 2018, 6(2): 306-316.

Hong T, Wang P, Willis H L. A naïve multiple linear regression benchmark for short term load forecasting[C]//2011 IEEE Power and Energy Society General Meeting. IEEE, 2011: 1-6.

Chen B J, Chang M W. Load forecasting using support vector machines: A study on EUNITE competition 2001[J]. IEEE transactions on power systems, 2004, 19(4): 1821-1830.

Barman M, Choudhury N B D, Sutradhar S. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India[J]. Energy, 2018, 145: 710-720.

Kuo P H, Huang C J. A high precision artificial neural networks model for short-term energy load forecasting[J]. Energies, 2018, 11(1): 213.

Wang Y, Gan D, Sun M, et al. Probabilistic individual load forecasting using pinball loss guided LSTM[J]. Applied Energy, 2019, 235: 10-20.

Wang S, Wang X, Wang S, et al. Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting[J]. International Journal of Electrical Power & Energy Systems, 2019, 109: 470-479.

11] He Y, Qin Y, Wang S, et al. Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network[J]. Applied energy, 2019, 233: 565-575.

Zhang W, Quan H, Srinivasan D. An improved quantile regression neural network for probabilistic load forecasting[J]. IEEE Transactions on Smart Grid, 2018, 10(4): 4425-4434.

Chen K, Chen K, Wang Q, et al. Short-term load forecasting with deep residual networks[J]. IEEE Transactions on Smart Grid, 2018, 10(4): 3943-3952.

Rafi S H, Deeba S R, Hossain E. A short-term load forecasting method using integrated CNN and LSTM network[J]. IEEE Access, 2021, 9: 32436-32448.

Khwaja A S, Anpalagan A, Naeem M, et al. Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting[J]. Electric Power Systems Research, 2020, 179: 106080.

Sajjad M, Khan Z A, Ullah A, et al. A novel CNN-GRU-based hybrid approach for short-term residential load forecasting[J]. Ieee Access, 2020, 8: 143759-143768.

Cao Z, Wan C, Zhang Z, et al. Hybrid ensemble deep learning for deterministic and probabilistic low-voltage load forecasting[J]. IEEE Transactions on Power Systems, 2019, 35(3): 1881-1897.

Bouktif S, Fiaz A, Ouni A, et al. Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches[J]. Energies, 2018, 11(7): 1636.

Rafiei M, Niknam T, Aghaei J, et al. Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 6961-6971.

Massaoudi M, Refaat S S, Chihi I, et al. A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for short-term load forecasting[J]. Energy, 2021, 214: 118874.

He F, Zhou J, Feng Z, et al. A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm[J]. Applied energy, 2019, 237: 103-116.

Sun M, Zhang T, Wang Y, et al. Using Bayesian deep learning to capture uncertainty for residential net load forecasting[J]. IEEE Transactions on Power Systems, 2019, 35(1): 188-201.

Ahmed M S, Cook A R. Analysis of freeway traffic time-series data by using Box-Jenkins techniques[M]. 1979.

Contreras J, Espinola R, Nogales F J, et al. ARIMA models to predict next-day electricity prices[J]. IEEE transactions on power systems, 2003, 18(3): 1014-1020.

Wei L, Zhen-gang Z. Based on time sequence of ARIMA model in the application of short-term electricity load forecasting[C]//2009 International Conference on Research Challenges in Computer Science. IEEE, 2009: 11-14.

Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50: 159-175.

He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.

K. Zhang, M. Sun, T. X. Han, X. Yuan, L. Guo and T. Liu, "Residual Networks of Residual Networks: Multilevel Residual Networks," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 6, pp. 1303-1314, June 2018, doi: 10.1109/TCSVT.2017.2654543.

Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708.

Zhou H, Zhang Y, Yang L, et al. Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism[J]. Ieee Access, 2019, 7: 78063-78074.

Kim T Y, Cho S B. Predicting residential energy consumption using CNN-LSTM neural networks[J]. Energy, 2019, 182: 72-81.

Tian C, Ma J, Zhang C, et al. A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network[J]. Energies, 2018, 11(12): 3493.

Han L, Peng Y, Li Y, et al. Enhanced deep networks for short-term and medium-term load forecasting[J]. Ieee Access, 2018, 7: 4045-4055.

Guo X, Zhao Q, Zheng D, et al. A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price[J]. Energy Reports, 2020, 6: 1046-1053.

Sheng Z, Wang H, Chen G, et al. Convolutional residual network to short-term load forecasting[J]. Applied Intelligence, 2021, 51(4): 2485-2499.