Load Forecasting Investigation for Efficient Photovoltaic Design in Owerri Metropolis

International Journal of Electrical and Electronics Engineering
© 2023 by SSRG - IJEEE Journal
Volume 10 Issue 6
Year of Publication : 2023
Authors : Okwe Gerald, Akwukwaegbu Isdore, Nosiri Onyebuchi, Obichere John Kennedy, Olubiwe Mathew, Okozi Samuel, Nwokonkwo Obi Chukwuemeka
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Okwe Gerald, Akwukwaegbu Isdore, Nosiri Onyebuchi, Obichere John Kennedy, Olubiwe Mathew, Okozi Samuel, Nwokonkwo Obi Chukwuemeka, "Load Forecasting Investigation for Efficient Photovoltaic Design in Owerri Metropolis," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 6, pp. 106-113, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I6P111

Abstract:

The quest towards achieving an alternative power supply in Nigeria to augment the depleted conventional fossil fuel source that is environmentally hostile and which never satisfies the energy needs of consumers necessitated this research. This paper focuses on load forecasting investigation for efficient photovoltaic design within the Owerri metropolis for ten years projected period (2020-2030). Two load forecasting techniques, linear regression and exponential smoothing, were adopted during the load forecasting investigation and simulated with the Matlab program. The results were compared to select the most suitable technique for forecasting long-term load. The investigations showed that the exponential smoothing technique could not be applied because of its unstable behaviour, which does not accurately represent system performance for long-term load forecasting. The satisfactory performance of the least square technique (linear regression) with an increase in value of the forecasted load 13692kW in the year 2020 to 13702kW in 2030, respectively, makes it most appropriate for the analysis and hence was adopted.

Keywords:

Exponential smoothing, Hybrid method, Least square regression, Load forecast, Solar sizing.

References:

[1] Uthman Salami, Ariemu Ogaga, and Dennis Matthew, “Power Outage: 5,000mw Electricity Generation Not Feasible by JULY 1- stakeholders,” Nigerian News Direct, 2022. [Online]. Available: https://nigeriannewsdirect.com/power-outage-5000mw-electricity-generation-not-feasible-by-july-1-stakeholders/
[2] Marin Matijas, “Electric Load Forecasting using Multivariate Meta-Learning”, Thesis, University of Zagreb, 2013.
[Google Scholar] [Publisher Link]
[3] Hong-ze Li et al., “A Hybrid Annual Power Load Forecasting Model Based on Generalized Regression Neural Network with Fruit Fly Optimization Algorithm,” Knowledge-Based Systems, vol. 37, pp. 378-387, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Medhat A. Rostum et al., “Electrical Load Forecasting: A Methodological overview,” International Journal of Engineering and Technology, vol. 9, no. 3, pp. 842-869, 2020.
[Google Scholar]
[5] Che Guan et al., “Very Short-Term Load Forecasting: Wavelet Neural Networks with Data Pre-Filtering,” IEEE Transactions on Power Systems, vol. 28, no. 1, pp. 30-41, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[6] A. E. Okoye, and T. C. Madueme, “A Theoretical Framework for Enhanced Forecasting of Electrical Loads,” International Journal of Scientific and Research Publications, vol. 6, no. 6, pp. 554-560, 2016.
[Google Scholar] [Publisher Link]
[7] Jianjun Wang et al., “An Annual Load Forecasting Model Based on Support Vector Regression with Differential Evolution Algorithm,” Applied Energy, vol. 94, pp. 65-70, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Zhiheng Zhang, and Shijie Ye, “Long-Term Load Forecasting and Recommendations for China Based on Support Vector Regression,” International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 597-602, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Papia Ray, Santanu Sen, and A. K. Barisal, “Hybrid Methodology for Short-Term Load Forecasting,” IEEE International Conference on Power Electronics, Drives and Energy Systems, India, pp. 1-6, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Shu Fan, and Rob J. Hyndman, “Short-Term Load Forecasting Based on A Semi-Parametric Additive Model,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 134-141, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Woo-Joo Lee, and Jinkyu Hong, “A Hybrid Dynamic and Fuzzy Time Series Model for Mid-Term Power Load Forecasting,” International Journal of Electrical Power & Energy Systems, vol. 64, pp. 1057-1062, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Wei-Chiang Hong, “Electric Load Forecasting by Seasonal Recurrent SVR (Support Vector Regression) with Chaotic Artificial Bee Colony Algorithm,” Energy, vol. 36, no. 9, pp. 5568-5578, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yoseba K. Penya, Cruz E. Borges, and Ivan Fernandez, “Short-Term Load Forecasting in Non-Residential Buildings,” IEEE Africon’11, Zambia, vol. 148, pp. 1-6, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Dilmurod Shukurov et al., “Synthesis of Zinc Phthalocyanine Pigment and its Application to New Generation Solar Cells,” International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 453-461, 2023.
[CrossRef] [Publisher Link]
[15] TCN Owerri, Annual Maintenance Report of Transmission Company of Nigeria Owerri Work Centre, 2021.
[16] I Okwe Gerald et al., “Modeling and Performance Assessment of 6mw Net-Metered Grid Tied Photovoltaic System for Owerri Metropolis,” IOP Conference Series: Earth and Environmental Science, vol. 730, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] N. Vidhya, and P. Rajadurai, “Modular Neural Network for Fault Detection and Classification in Photovoltaic System,” DS Journal of Digital Science and Technology, vol. 1, no. 1, pp. 17-22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Nigerian Electricity System Operators. Electric Power Monitoring and Control Retrieved, May 21, 2023, [Online]. Available: https://nsong.org/Library.
[19] Mohammad Abazid, Anees Abdulrahman, and Siham Samine, “Least Squares Methods to Forecast Sales for Company,” International Journal of Scientific & Engineering Research, vol. 9, no. 6, pp. 864-868, 2018.
[Google Scholar] [Publisher Link]
[20] Steven J. Miller, “The Method of Least Squares,” Thesis, Williams College, Williamstown, 2006.
[Google Scholar] [Publisher Link]
[21] O. S. Ezennaya et al., “Analysis of Nigeria’s National Electricity Demand Forecast (2013–2030),” International Journal of Science and Technology Research, vol. 3, no. 3, pp. 333-340, 2014.
[Google Scholar] [Publisher Link]
[22] S. L. Braide, and E. J. Diema, “Analysis of Least Square and Exponential Regression Techniques for Energy Demand Requirement (2013-2032),” American Journal of Electrical and Electronic Engineering, vol. 6, no. 2, pp. 38-59, 2018.
[Google Scholar] [Publisher Link]
[23] M. Dhanalakshmi, and V. Radha, “Novel Regression and Least Square Support Vector Machine Learning Technique for Air Pollution Forecasting,” International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 147-158, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Rafal Weron, Modeling and Forecasting Electricity Loads and Prices A Statistical Approach, John Wiley & Sons, 2006.
[Google Scholar] [Publisher Link]
[25] Eva Ostertagová1, and Oskar Ostertag, “The Simple Exponential Smoothing Model,” The 4th International Conference on Modelling of Mechanical and Mechatronic Systems, Technical University of Košice, Slovak Republic, pp. 380-384, 2011.
[Google Scholar] [Publisher Link]
[26] Papia Ray, Santanu Sen, and A. K. Barisal, “Hybrid Methodology for Short-Term Load Forecasting,” IEEE International Conference on Power Electronics, Drives and Energy Systems, India, pp. 1-6, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Shu Fan, and Rob J. Hyndman, “Short-Term Load Forecasting Based on A Semi-Parametric Additive Model,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 134-141, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Woo-Joo Lee, and Jinkyu Hong, “A Hybrid Dynamic and Fuzzy Time Series Model for Mid-Term Power Load Forecasting,” International Journal of Electrical Power & Energy Systems, vol. 64, pp. 1057-1062, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Arunesh Kumar Singh et al., “Load Forecasting Techniques and Methodologies A Review,” 2nd International Conference on Power, Control and Embedded Systems, India, pp. 1-10, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Arunesh Kumar Singh et al., “An Overview of Electricity Demand Forecasting Techniques,” Network and Complex Systems, vol. 3, no. 2, pp. 38-48, 2013.
[Google Scholar] [Publisher Link]