NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM
Keywords:Genetic algorithm, least squares support vector machine, Next-hour, electricity price forecasting
Predicting the price of electricity is crucial for the operation of power systems. Short-term electricity price forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead forecasts offer expected prices to market participants before operation hours. This is especially useful for effective bidding strategies where the bidding amount can be reviewed or changed before the operation hours. Nevertheless, many existing models have relatively low prediction accuracy. Furthermore, single prediction models are typically less accurate for different scenarios. Thus, a hybrid model comprising least squares support vector machine (LSSVM) and genetic algorithm (GA) was developed in this work to predict electricity prices with higher accuracy. This model was tested on the Ontario electricity market. The inputs, which were the hourly Ontario electricity price (HOEP) and demand for the previous seven days, as well as 1-h pre-dispatch price (PDP), were optimized by GA to prevent losing potentially important inputs. At the same time, the LSSVM parameters were optimized by GA to obtain accurate forecasts. The hybrid LSSVM-GA model was shown to produce an average mean absolute percentage error (MAPE) of 8.13% and the structure of this model is less complex compared with other models developed in previous studies. This is due to the fact that only two algorithms were used (LSSVM and GA), with the load and HOEP for the week preceding the forecasting hour as the inputs. Based on the results, it is concluded that the proposed hybrid algorithm is a promising alternative to produce good electricity price forecasts.
H. Zareipour, K. Bhattacharya, and C. a. Canizares, 2006,"Forecasting the hourly Ontario energy price by multivariate adaptive regression splines," in 2006 IEEE Power Engineering Society General Meeting, 1-7, https://doi.org/10.1109/PES.2006.1709474
M. Narajewski and F. Ziel, 2020, "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, 279(April): 115801, https://doi.org/10.1016/j.apenergy.2020.115801
A. R. Khan, A. Mahmood, A. Safdar, Z. A. Khan, and N. A. Khan, 2016, "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, 54: 1311-1322, doi: https://doi.org/10.1016/j.rser.2015.10.117
C. Zhang, R. Li, H. Shi, and F. Li, 2020, "Deep learning for day‐ahead electricity price forecasting," IET Smart Grid, 3(4): 462-469. doi: https://doi.org/10.1049/iet-stg.2019.0258
G. Marcjasz, 2020, "Forecasting electricity prices using deep neural networks: A robust hyper-parameter selection scheme," Energies, 13(18): 1-18, doi: https://doi.org/10.3390/en13184605
K. B. Sahay, 2015,"One hour ahead price forecast of Ontario electricity market by using ANN," in 2015 International Conference on Energy Economics and Environment (ICEEE), Mar. 1-6, doi: https://doi.org/10.1109/EnergyEconomics.2015.7235102
V. Sharma and D. Srinivasan, "A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market," Engineering Applications of Artificial Intelligence, 26(5-6): 1562-1574, 2013, doi: https://doi.org/10.1016/j.engappai.2012.12.012
L. Wu and M. Shahidehpour, 2010. "A Hybrid Model for Day-Ahead Price Forecasting," Engineering Applications of Artificial Intelligence, 25(3): 1519-1530. https://doi.org/10.1109/TPWRS.2009.2039948
D. Mirikitani and N. Nikolaev, 2011"Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks," Neural Computing and Applications, 20(1): 79-89, Feb., doi: https://doi.org/10.1007/s00521-010-0344-1
G. Memarzadeh and F. Keynia, 2021, "Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm," Electric Power Systems Research 192(July): 106995, doi: https://doi.org/10.1016/j.epsr.2020.106995
A. Heydari, M. Majidi Nezhad, E. Pirshayan, D. Astiaso Garcia, F. Keynia, and L. De Santoli, 2020, "Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm," Applied Energy, 277(January): 115503, doi: https://doi.org/10.1016/j.apenergy.2020.115503
C. Lee and C. Wu, 2020."Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network," Energies, 13(17), 4408. https://doi.org/10.3390/en13174408
M. Halu, M. Verbi, and J. Zori, 2020, "Performance of alternative electricity price forecasting methods: Findings from the Greek and Hungarian power exchanges," Applied Energy, 277(April): 115599. https://doi.org/10.1016/j.apenergy.2020.115599
A. Ghasemi-Marzbali, 2020,"A developed short-term electricity price and load forecasting method based on data processing, support vector machine, and virus colony search," Energy Efficiency, 13(7): 1525-1542, doi: https://doi.org/10.1007/s12053-020-09898-w
Y. Zhang, C. Deng, R. Zhao, and S. Leto, 2020,"A novel integrated price and load forecasting method in smart grid environment based on multi-level structure," Engineering Applications of Artificial Intelligence, 95: 103852 doi: 10.1016/j.engappai.2020.103852.
G. Xie, S. Wang, Y. Zhao, and K. K. Lai, 2013,"Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study," Applied Soft Computing, 13(5): 2232-2241, doi: 10.1016/j.asoc.2013.02.002.
H. Wang and D. Hu, 2005, "Comparison of SVM and LS-SVM for Regression," in 2005 International Conference on Neural Networks and Brain, 5: 279-283.
S. Li and L. Dai, 2012,"Classification of gasoline brand and origin by Raman spectroscopy and a novel R-weighted LSSVM algorithm," Fuel, 96: 146-152, doi: 10.1016/j.fuel.2012.01.001.
J. H. Holland, 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence.
E. Elbeltagi, T. Hegazy, and D. Grierson, 2005, "Comparison among five evolutionary-based optimization algorithms," Adv. Eng. Informatics, 19(1): 43-53 doi: 10.1016/j.aei.2005.01.004.
D. Zhijie, Li; Xiangdong, 2010, v Comparative Research on Particle Swarm Optimization and Genetic AlgorithmLiu; Xiangdon, "Comparative Research on Genetic Algorithm, Particle Swarm Optimization and Hybrid GA-PSO," in Computer and Information Science, 3: 120-127.https://doi.org/10.5539/cis.v3n1p120
I. Azmira et al., 2017. "Short Term Electricity Price Forecasting with Multistage Optimization Technique of LSSVM-GA," Journal of Telecommunication, Electronic and Computer Engineering 9(2-7) : 1-6