A Hybrid Prediction Model for Short-Term Load Forecasting in Power Systems
Article Sidebar
Main Article Content
Abstract
Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By integrating the Salp Swarm Algorithm (SSA) with Least Squares Support Vector Machines (LSSVM), the iSSA-LSSVM model significantly improves LSSVM's prediction accuracy. One of the key contributions is the model's ability to autonomously ne-tune LSSVM hyperparameters, eliminating the need for manual adjustments and optimizing performance. Modifying the SSA within iSSA-LSSVM enhances the original algorithm's exploration and exploitation capabilities, ensuring better search efficiency and precision. Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. Comparative analysis with three other models shows that iSSA-LSSVM achieves a lower Mean Square Error (MSE) and faster convergence. This improvement in accuracy and efficiency enhances STLF, allowing power dispatch centers to develop more precise generation plans and ensure more reliable power system operation.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
S. M. Sulaiman, P. A. Jeyanthy, D. Devaraj and K. V. Shihabudheen, “A novel hybrid short-term electricit forecasting technique for residential loads using Empirical Mode Decomposition and Extreme Learning Machines,” Computers & Electrical Engineering, vol. 98, p. 107663, 2022.
X. He, W. Zhao, Z. Gao, Q. Zhang and W. Wang, “A hybrid prediction interval model for short-term electric load forecast using Holt-Winters and Gate Recurrent Unit,” Sustainable Energy, Grids and Networks, vol. 38, p. 101343, 2024.
R. Wazirali, E. Yaghoubi, M. S. S. Abujazar, R. Ahmad and A. H. Vakili, “State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques,” Electric Power Systems Research, vol. 225, p. 109792, 2023.
Y. Wang, S. Sun, G. Fathi and M. Eslami, “Improving the Method of Short-term Forecasting of Electric Load in Distribution Networks using Wavelet transform combined with Ridgelet Neural Network Optimized by Self-adapted Kho-Kho Optimization Algorithm,” Heliyon, vol. 10, no. 7, p. e28381, 2024.
S. Williams and M. Short, “Electricity demand forecasting for decentralised energy management,” Energy and Built Environment, vol. 1, no. 2, pp. 178-186, 2020.
S. Nazir, H. Hamdoun, O. Al-Azubi and J. Alzubi, “Cyber Attack Challenges and Resilience for Smart Grids,” European Journal of Scientific Research, vol. 134, no. 1, pp. 111-120, 2015.
A. Jahani, K. Zare, and L. M. Khanli, “Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM,” Sustainable Cities and Society, vol. 98, p. 104775, 2023.
H. Wang, K. A. Alattas, A. Mohammadzadeh, M. H. Sabzalian, A. A. Aly and A. Mosavi, “Comprehensive review of load forecasting with emphasis on intelligent computing approaches,” Energy Reports, vol. 8, pp. 13189-13198, 2022.
S. Zhang, R. Chen, J. Cao, and J. Tan, “A CNN and LSTM-based multi-task learning architecture for short and medium-term electricity load forecasting,” Electric Power Systems Research, vol. 222, p. 109507, 2023.
C. Tarmanini, N. Sarma, C. Gezegin and O. Ozgonenel, “Short term load forecasting based on ARIMA and ANN approaches,” Energy Reports, vol. 9, pp. 550-557, 2023.
H. A. Phan et al., “Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning,” ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 18, no. 1, pp. 64 - 75, 2024.
A. Dey, S. Biswas and L. Abualigah, “Efficient Violence Recognition in Video Streams using ResDLCNN-GRU Attention Network,” ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 18, no. 3, pp. 329-341, 2024.
C. Li, Z. Li, H. Zhu, Z. Tian, and W. Feng, “Study on operation strategy and load forecasting for distributed energy system based on Chinese supply-side power grid reform,” Energy and Built Environment, vol. 3, no. 1, pp. 113-127, 2022.
G.-F. Fan, Y.-Y. Han, J.-W. Li, L.-L. Peng, Y.- H. Yeh and W.-C. Hong, “A hybrid model for deep learning short-term power load forecasting based on feature extraction statistics techniques,” Expert Systems with Applications, vol. 238, p. 122012, 2024.
M. Zhang et al., “Accurate ultra-short-term load forecasting based on load characteristic decomposition and convolutional neural network with bidirectional long short-term memory model,” Sustainable Energy, Grids and Networks, vol. 35, p. 101129, 2023.
A. Alhendi, A. S. Al-Sumaiti, M. Marzband, R. Kumar and A. A. Z. Diab, “Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England,” Energy Reports, vol. 9, pp. 4799-4815, 2023.
X. Xiao, H. Mo, Y. Zhang and G. Shan, “Meta- ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting,” Energy, vol. 246, p. 123418, 2022.
L. B. S. Morais, G. Aquila, V. A. D. de Faria, L. M. M. Lima, J. W. M. Lima and A. R. deQueiroz, “Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system,” Applied Energy, vol. 348, p. 121439, 2023.
A. A. Movassagh, A. Alzubi, Jafar,, M. Gheisari, M. Rahimi, M. S. Kumar, A. A. Abbasi and N. Nabipour, “Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model,” Journal of Ambient Intelligence Humanized Computing, vol. 14, pp. 6017-6025, 2023.
D. Liu and H. Wang, “Time series analysis model for forecasting unsteady electric load in buildings,” Energy and Built Environment, vol. 5, no. 6, pp. 900-910, 2023.
J. Yuan, L. Wang, Y. Qiu, J. Wang, H. Zhang and Y. Liao, “Short-term electric load forecasting based on improved Extreme Learning Machine Mode,” Energy Reports, vol. 7, pp. 1563-1573, 2021.
S. Li, L. Goel and P. Wang, “An ensemble approach for short-term load forecasting by extreme learning machine,” Applied Energy, vol. 170, pp. 22-29, 2016.
C. Chupong and B. Plangklang, “Incremental Learning Model for Load Forecasting without Training Sample,” Computers, Materials & Continua, vol. 72, no. 3, pp. 5415-5427, 2022.
N. Zeng, H. Zhang, W. Liu, J. Liang and F. E. Alsaadi, “A switching delayed PSO optimized extreme learning machine for short-term loadforecasting,” Neurocomputing, vol. 240, pp. 175-182, 2017.
S. Li et al., “Short-term electrical load forecasting using hybrid model of manta ray foraging optimization and support vector regression,” Journal of Cleaner Production, vol. 388, p. 135856, 2023.
S. Li, H. Chen, M. Wang, A. A. Heidari and S. Mirjalili, “Slime mould algorithm: A new method for stochastic optimization,” Future Generation Computer Systems, vol. 111, pp. 300-323, 2020.
S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-Based Systems, vol. 89, pp. 228-249, 2015.
M. Zulfiqar, M. Kamran, M. B. Rasheed, T. Alquthami and A. H. Milyani, “Hyperparameter optimization of support vector machine using adaptive differential evolution for electricity load forecasting,” Energy Reports, vol. 8, pp. 13333-13352, 2022.
X. Han, Y. Shi, R. Tong, S. Wang and Y. Zhang, “Research on short-term load forecasting of power system based on IWOA-KELM,” Energy Reports, vol. 9, pp. 238-246, 2023.
I. A. Ibrahim and M. J. Hossain, “Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models,” Energy Conversion and Management, vol. 296, p. 117663, 2023.
H. Xu, G. Fan, G. Kuang, and Y. Song, “Construction and Application of Short-Term and Mid-Term Power System Load Forecasting Model Based on Hybrid Deep Learning,” IEEE Access, vol. 11, pp. 37494-37507, 2023.
C. A. Saleel, “Forecasting the energy output from a combined cycle thermal power plant using deep learning models,” Case Studies in Thermal Engineering, vol. 28, p. 101693, 2021.
T. Danesh, R. Ouaret, P. Floquet, and S. Negny, “Hybridization of model-specific and model agnostic methods for interpretability of Neural network predictions: Application to a power plant,” Computers & Chemical Engineering, vol. 176, p. 108306, 2023.
P. T¨ufekci, “Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods,” International Journal of Electrical Power & Energy Systems, vol. 60, pp. 126-140, 2014.
X. Zhang, J. Wang and K. Zhang, “Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm,” Electric Power Systems Research, vol. 146, pp. 270-285, 2017.
H. A. Phan et al., “Development of an Autonomous Component Testing System with Reliability Improvement using Computer Vision and Machine Learning,” ECTI Transactions on Computer and Information Technology, vol. 18, no. 1, pp. 64-75, 2024.
M. Barman, N. B. Dev Choudhury and S. Sutradhar, “A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India,” Energy, vol. 145, pp. 710-720, 2018.
M. Barman and N. B. Dev Choudhury, “Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept,” Energy, vol. 174, pp. 886-896, 2019.
B. Beiranvand and T. Rajaee, “Application of artificial intelligence-based single and hybrid models in predicting seepage and pore water pressure of dams: A state-of-the-art review,” Advances in Engineering Software, vol. 173, p. 103268, 2022.
S. Samantaray, S. Sawan Das, A. Sahoo and D. Prakash Satapathy, “Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm,” Ain Shams Engineering Journal, Vol. 13, no. 5, p. 101732, 2022.
J. Lu, J. Yue, L. Zhu, D. Wang and G. Li, “An improved variational mode decomposition method based on the optimization of salp swarm algorithm used for denoising of natural gas pipeline leakage signal,” Measurement, vol. 185, p. 110107, 2021.
S. Zhao, P. Wang, A. A. Heidari, H. Chen, W. He and S. Xu, “Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy,” Computers in Biology and Medicine, vol. 139, p. 105015, 2021.
J. Cervantes, F. Garcia-Lamont, L. Rodr´ıguez Mazahua and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189-215, 2020.
S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris and S. M. Mirjalili, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,” Advances in Engineering Software, vol. 114, pp. 163-191, 2017.
M. H. Sulaiman and Z. Mustaffa, “An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 5, p. 100195, 2023.
T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh and S. Mirjalili, “Particle Swarm Optimization: A Comprehensive Survey,” in IEEE Access, vol. 10, pp. 10031-10061, 2022.
A. Huong, K. G. Tay, N. A. Jumadi, W. M. H. W. Mahmud and X. Ngu, “DFU Infection and Ischemia Classification: PSO-Optimized Deep Learning Networks,” —em Applications of Modelling and Simulation, vol. 7, no. 2023, pp. 111-121, 2023.
A. Hamdan, S. S. Nah, G. S. Leng, C. K. Leng and T. W. King, “Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem,” Applications of Modelling and Simulation, vol. 7, no. 2023, pp. 214-238, 2023.