PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

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• Al Amin, M. A., & Hoque, M. A. (2019). Comparison of ARIMA and SVM for short-term load forecasting. In 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/IEMECONX.2019.8877077

• Avazov, N., Liu, J., & Khoussainov, B. (2019). Periodic neural networks for multivariate time series analysis and forecasting. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE Publishing. https://doi.org/10.1109/IJCNN.2019.8851710

• Babcock, C., Matney, J., Finley, A. O., Weiskittel, A., & Cook, B. D. (2013). Multivariate spatial regression models for predicting individual tree structure variables using LiDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(1), 6-14. https://doi.org/10.1109/JSTARS.2012.2215582

• Carvallo, J. P., Larsen, P. H., Sanstad, A. H., & Goldman, C. A. (2016). Load forecasting in electric utility integrated resource planning. LBNL Publications.

• El Kafazi, I., Bannari, R., Abouabdellah, A., Aboutafail, M. O., & Guerrero, J. M. (2017). Energy production: A comparison of forecasting methods using the polynomial curve fitting and linear regression. In 2017 International Renewable and Sustainable Energy Conference (IRSEC) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/IRSEC.2017.8477278

• Fu, Q., Lai, R., Shan, Y., & Geng, X. (2018). A spatial forecasting method for photovoltaic power generation combined of improved similar historical days and dynamic weights allocation. In 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) (pp. 1195-1198). IEEE Publishing. https://doi.org/10.1109/ISGT-Asia.2018.8467889

• Jimenez, J., Pertuz, A., Quintero, C., & Montana, J. (2019). Multivariate statistical analysis based methodology for long-term demand forecasting. IEEE Latin America Transactions, 17(01), 93-101. https://doi.org/10.1109/TLA.2019.8826700

• Kartikasari, M. D., & Prayogi, A. R. (2018). Demand forecasting of electricity in Indonesia with limited historical data. Journal of Physics: Conference Series, 974, Article 012040. https://doi.org/10.1088/1742-6596/974/1/012040

• Kobylinski, P., Wierzbowski, M., & Piotrowski, K. (2020). High-resolution net load forecasting for micro-neighbourhoods with high penetration of renewable energy sources. International Journal of Electrical Power & Energy Systems, 117, Article 105635. https://doi.org/10.1016/j.ijepes.2019.105635

• Lagaaij, A. (2018). Accelerating solar for decelerating climate change in time. In 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) (pp. 2392-2394). IEEE Publishing. https://doi.org/10.1109/PVSC.2018.8547702

• Mukhopadhyay, P., Mitra, G., Banerjee, S., & Mukherjee, G. (2017). Electricity load forecasting using fuzzy logic: Short term load forecasting factoring weather parameter. In 2017 7th International Conference on Power Systems (ICPS) (pp. 812-819). IEEE Publishing. https://doi.org/10.1109/ICPES.2017.8387401

• Raza, M. Q., Mithulananthan, N., Li, J., & Lee, K. Y. (2020). Multivariate ensemble forecast framework for demand prediction of anomalous days. IEEE Transactions on Sustainable Energy, 11(1), 27-36. https://doi.org/10.1109/TSTE.2018.2883393

• Sun, X., Ouyang, Z., & Yue, D. (2017). Short-term load forecasting based on multivariate linear regression. In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/EI2.2017.8245401