Supporting Vector Regression Hybridization with chaotic Algorithm for Electric Load Prediction in Southern Region
Article Sidebar
Main Article Content
Abstract
Abstract:
Electric energy is one of the most important main sources of energy that contributes significantly to all sectors that push the development process to progress. It plays an important role in the process of development and economic and cultural well-being to be used as important requirements in the economy. Therefore, it is one of the most important manifestations of civilization and development and a measure of
sophistication and well-being in any society because of the services
provided by this energy.
This study applies a model for electric load prediction by applying support vector regression with chaotic hybrid algorithms to improve prediction performance, Which solves the problems of improving the support vector regression parameters, so the support vector regression was hybridized with chaotic algorithms (CIA), (CPSO) to determine the optimal and appropriate parameters for the support vector regression model, Comparing the models with each other, to choose the best model and use it to predict electrical energy consumption for the period (2020-2029) in the southern region of Iraq. The results found that the CIASVR model is more accurate and efficient than other forecasting models based on statistical forecasting accuracy criteria.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
References
Hussein, Jassim Nasser et al., "using the self-regression model and integrated mobile media to predict the production of dates in Iraq", University of Basra, Gulf Economist magazine, issue (42), December, 2019
Hussein, Sahra Zain the use of the ARIMA method in predicting the money supply in the Iraqi economy, Faculty of management and Economics University of Basra Gulf economic Magazine, Issue (35), March 2018
Mohammed, Saleh Raed and Saleh, Ibrahim Ahmed (2018). "Using the swarm element optimization algorithm to measure software quality", al-Rafidain Journal of Computer Science and mathematics, Vol. (12), No. (1).
Chen, C. W. & Chang, Y. C. (2016). "Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation", National Taipei University of Technology, Hindawi Publishing Corporation, Volume 2016, No. 1.
Cheng, Y., Fu, Y., & Gong, G. (2010). "A Hybrid Support Vector Regression Based on Chaotic Particle Swarm Optimization Algorithm in Forecasting Financial Returns", School of Computer Science & Technology, Soochow University, China.
Hong, W. C., Dong, Y., Lai, C. Y., Chen, L. Y. & Wei, S. Y. (2011). "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting”, Energies, 4(6), pp (960–977).
Hong, W.-C. (2013). Intelligent Energy Demand Forecasting. Department of Information Management Oriental Institute of Technology New Taipei City Taiwan R.O.C, Lecture Notes in Energy. doi:10.1007/978-1-4471-4968-2.
Hussein, Jassim Nasser et al., "Using the Autoregressive Model and Integrated Moving Media (ARMA) to Predict Date Production in Iraq", University of Basra, Gulf Economic Journal, Issue (42), December, 2019.
Kavzoglu, T., & Colkesen, I. (2009). "A kernel functions analysis for support vector machines for land cover classification". International Journal of Applied Earth Observation and Geoinformation, vol.11, No. Li, T.
Yong.W.; Zhi, GZ.; Zhi, L.; Chen, YG. (2014). Short-Term Wind Power Prediction Based on Support Vector Regress Optimized by Chaos Particle Swarm Optimization, School of Electrical Engineering, Anhui Polytechnic University, Wuhu, China,
Liu, B., Wang, L., Jin, Y.-H., Tang, F., & Huang, D.-X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261–1271. doi: 10.1016/j.chaos.2004.11.095.
Mohammed, Salha Raed and Saleh, Ibrahim Ahmed (2018). "Using Swarm Element Optimization Algorithm to Measure SoftwareQuality", Al-Rafidain Journal of Computer Science and Mathematics,Vol. (12), No. (1), Prakash,
A., Kiwanis, N., Tiwari, M.K.& Cohen, Y. (2008). "Modified immune algorithm for job selection and operation allocation problem in flexible manufacturing system", Advances in Engineering Software,
Sarhani ،M., El Afia, A. (2015). "Electric load forecasting using hybrid machine learning approach incorporating feature selection", Mohammed-V University Rabat, Morocco,
Wang, Y., Wang, B. & Zhang, X. (2012). "A New Application of the Support Vector Regression on the Construction of Financial Conditions Index to CPI Prediction", Procedia Computer Science, vols. 9,
Yuan, F.C. (2012). "Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting", Department of Information Management, Yuan Ze University, 2012, vols. 3,
Zain ،Sahara Hussein، Using the ARIMA method in forecasting money Supply in the Iraqi economy، University of Basrah Gulf Economic Journal, Issue (35), March, 2018